Information loss in approximately bayesian data assimilation: a comparison of generative and discriminative approaches to estimating agricultural yield

USDA-ARS?s Scientific Manuscript database

Data assimilation and regression are two commonly used methods for predicting agricultural yield from remote sensing observations. Data assimilation is a generative approach because it requires explicit approximations of the Bayesian prior and likelihood to compute the probability density function...

L-band Microwave Remote Sensing and Land Data Assimilation Improve the Representation of Prestorm Soil Moisture Conditions for Hydrologic Forecasting

NASA Technical Reports Server (NTRS)

Crow, W. T.; Chen, F.; Reichle, R. H.; Liu, Q.

2017-01-01

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events.

L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting.

PubMed

Crow, W T; Chen, F; Reichle, R H; Liu, Q

2017-06-16

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events.

L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting

PubMed Central

Crow, W.T.; Chen, F.; Reichle, R.H.; Liu, Q.

2018-01-01

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events. PMID:29657342

Assimilating Leaf Area Index Estimates from Remote Sensing into the Simulations of a Cropping Systems Model

USDA-ARS?s Scientific Manuscript database

Spatial extrapolation of cropping systems models for regional crop growth and water use assessment and farm-level precision management has been limited by the vast model input requirements and the model sensitivity to parameter uncertainty. Remote sensing has been proposed as a viable source of spat...

Optimality in Data Assimilation

NASA Astrophysics Data System (ADS)

Nearing, Grey; Yatheendradas, Soni

2016-04-01

It costs a lot more to develop and launch an earth-observing satellite than it does to build a data assimilation system. As such, we propose that it is important to understand the efficiency of our assimilation algorithms at extracting information from remote sensing retrievals. To address this, we propose that it is necessary to adopt completely general definition of "optimality" that explicitly acknowledges all differences between the parametric constraints of our assimilation algorithm (e.g., Gaussianity, partial linearity, Markovian updates) and the true nature of the environmetnal system and observing system. In fact, it is not only possible, but incredibly straightforward, to measure the optimality (in this more general sense) of any data assimilation algorithm as applied to any intended model or natural system. We measure the information content of remote sensing data conditional on the fact that we are already running a model and then measure the actual information extracted by data assimilation. The ratio of the two is an efficiency metric, and optimality is defined as occurring when the data assimilation algorithm is perfectly efficient at extracting information from the retrievals. We measure the information content of the remote sensing data in a way that, unlike triple collocation, does not rely on any a priori presumed relationship (e.g., linear) between the retrieval and the ground truth, however, like triple-collocation, is insensitive to the spatial mismatch between point-based measurements and grid-scale retrievals. This theory and method is therefore suitable for use with both dense and sparse validation networks. Additionally, the method we propose is *constructive* in the sense that it provides guidance on how to improve data assimilation systems. All data assimilation strategies can be reduced to approximations of Bayes' law, and we measure the fractions of total information loss that are due to individual assumptions or approximations in the prior (i.e., the model uncertainty distribution), and in the likelihood (i.e., the observation operator and observation uncertainty distribution). In this way, we can directly identify the parts of a data assimilation algorithm that contribute most to assimilation error in a way that (unlike traditional DA performance metrics) considers nonlinearity in the model and observation and non-optimality in the fit between filter assumptions and the real system. To reiterate, the method we propose is theoretically rigorous but also dead-to-rights simple, and can be implemented in no more than a few hours by a competent programmer. We use this to show that careful applications of the Ensemble Kalman Filter use substantially less than half of the information contained in remote sensing soil moisture retrievals (LPRM, AMSR-E, SMOS, and SMOPS). We propose that this finding may explain some of the results from several recent large-scale experiments that show lower-than-expected value to assimilating soil moisture retrievals into land surface models forced by high-quality precipitation data. Our results have important implications for the SMAP mission because over half of the SMAP-affiliated "early adopters" plan to use the EnKF as their primary method for extracting information from SMAP retrievals.

Assimilation of Satellite-Derived Precipitation into the Regional Atmospheric Model System (RAMS): Its Impacts on the Weather and Hydrology in the Southwest United States

NASA Astrophysics Data System (ADS)

Yi, H.; Gao, X.; Sorooshian, S.

2002-05-01

As one aspect of the study of interactions between the atmosphere, vegetation, soil, and hydrology, there has been on going efforts to assimilate soil moisture data using coupled and uncoupled land surface-atmosphere hydrology models. The assimilation of soil moisture is expected to have influence due to its vital function in regulating runoff, partitioning latent and sensible heat, and through determining groundwater recharge. Soil moisture can provides long-term memory or persistence of the surface boundary condition, influencing large-scale atmospheric circulation over subsequent intervals. Now that the application of satellite remote sensing has become obvious to provide input parameters associated with land surface processes to the numerical models, this study utilizes remotely sensed precipitation data, PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) to assimilate soil moisture and other soil surface characteristics. Compared to the other earlier modeling experiments of seasonal or interannual temporal scale in continental or global spatial scale, this study investigates short term predictability in regional scale with the southwest United States as a study area, which has unique metrological and geographical features that provide special difficulties for mesoscale modeling. Research objectives are to assimilate the PERSIANN precipitation data into the mesoscale model for model initialization, examine the influence and memory of model precipitation errors on the land surface and atmospheric processes, and thereby study the short term predictability of meteorology and hydrology in the Southwest United States.

Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes

USDA-ARS?s Scientific Manuscript database

Assimilation of remotely sensed soil moisture data (SM-DA) to correct soil water stores of rainfall-runoff models has shown skill in improving streamflow prediction. In the case of large and sparsely monitored catchments, SM-DA is a particularly attractive tool.Within this context, we assimilate act...

The auto-tuned land data assimilation system (ATLAS)

USDA-ARS?s Scientific Manuscript database

Land data assimilation systems are tasked with the merging remotely-sensed soil moisture retrievals with information derived from a soil water balance model driven (principally) by observed rainfall. The performance of such systems is frequently degraded by the imprecise specification of parameters ...

Development and implementation of a remote-sensing and in situ data-assimilating version of CMAQ for operational PM2.5 forecasting. Part 1: MODIS aerosol optical depth (AOD) data-assimilation design and testing.

PubMed

McHenry, John N; Vukovich, Jeffery M; Hsu, N Christina

2015-12-01

This two-part paper reports on the development, implementation, and improvement of a version of the Community Multi-Scale Air Quality (CMAQ) model that assimilates real-time remotely-sensed aerosol optical depth (AOD) information and ground-based PM2.5 monitor data in routine prognostic application. The model is being used by operational air quality forecasters to help guide their daily issuance of state or local-agency-based air quality alerts (e.g. action days, health advisories). Part 1 describes the development and testing of the initial assimilation capability, which was implemented offline in partnership with NASA and the Visibility Improvement State and Tribal Association of the Southeast (VISTAS) Regional Planning Organization (RPO). In the initial effort, MODIS-derived aerosol optical depth (AOD) data are input into a variational data-assimilation scheme using both the traditional Dark Target and relatively new "Deep Blue" retrieval methods. Evaluation of the developmental offline version, reported in Part 1 here, showed sufficient promise to implement the capability within the online, prognostic operational model described in Part 2. In Part 2, the addition of real-time surface PM2.5 monitoring data to improve the assimilation and an initial evaluation of the prognostic modeling system across the continental United States (CONUS) is presented. Air quality forecasts are now routinely used to understand when air pollution may reach unhealthy levels. For the first time, an operational air quality forecast model that includes the assimilation of remotely-sensed aerosol optical depth and ground based PM2.5 observations is being used. The assimilation enables quantifiable improvements in model forecast skill, which improves confidence in the accuracy of the officially-issued forecasts. This helps air quality stakeholders be more effective in taking mitigating actions (reducing power consumption, ride-sharing, etc.) and avoiding exposures that could otherwise result in more serious air quality episodes or more deleterious health effects.

Moving horizon estimation for assimilating H-SAF remote sensing data into the HBV hydrological model

NASA Astrophysics Data System (ADS)

Montero, Rodolfo Alvarado; Schwanenberg, Dirk; Krahe, Peter; Lisniak, Dmytro; Sensoy, Aynur; Sorman, A. Arda; Akkol, Bulut

2016-06-01

Remote sensing information has been extensively developed over the past few years including spatially distributed data for hydrological applications at high resolution. The implementation of these products in operational flow forecasting systems is still an active field of research, wherein data assimilation plays a vital role on the improvement of initial conditions of streamflow forecasts. We present a novel implementation of a variational method based on Moving Horizon Estimation (MHE), in application to the conceptual rainfall-runoff model HBV, to simultaneously assimilate remotely sensed snow covered area (SCA), snow water equivalent (SWE), soil moisture (SM) and in situ measurements of streamflow data using large assimilation windows of up to one year. This innovative application of the MHE approach allows to simultaneously update precipitation, temperature, soil moisture as well as upper and lower zones water storages of the conceptual model, within the assimilation window, without an explicit formulation of error covariance matrixes and it enables a highly flexible formulation of distance metrics for the agreement of simulated and observed variables. The framework is tested in two data-dense sites in Germany and one data-sparse environment in Turkey. Results show a potential improvement of the lead time performance of streamflow forecasts by using perfect time series of state variables generated by the simulation of the conceptual rainfall-runoff model itself. The framework is also tested using new operational data products from the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) of EUMETSAT. This study is the first application of H-SAF products to hydrological forecasting systems and it verifies their added value. Results from assimilating H-SAF observations lead to a slight reduction of the streamflow forecast skill in all three cases compared to the assimilation of streamflow data only. On the other hand, the forecast skill of soil moisture shows a significant improvement.

Assimilation of LAI time-series in crop production models

NASA Astrophysics Data System (ADS)

Kooistra, Lammert; Rijk, Bert; Nannes, Louis

2014-05-01

Agriculture is worldwide a large consumer of freshwater, nutrients and land. Spatial explicit agricultural management activities (e.g., fertilization, irrigation) could significantly improve efficiency in resource use. In previous studies and operational applications, remote sensing has shown to be a powerful method for spatio-temporal monitoring of actual crop status. As a next step, yield forecasting by assimilating remote sensing based plant variables in crop production models would improve agricultural decision support both at the farm and field level. In this study we investigated the potential of remote sensing based Leaf Area Index (LAI) time-series assimilated in the crop production model LINTUL to improve yield forecasting at field level. The effect of assimilation method and amount of assimilated observations was evaluated. The LINTUL-3 crop production model was calibrated and validated for a potato crop on two experimental fields in the south of the Netherlands. A range of data sources (e.g., in-situ soil moisture and weather sensors, destructive crop measurements) was used for calibration of the model for the experimental field in 2010. LAI from cropscan field radiometer measurements and actual LAI measured with the LAI-2000 instrument were used as input for the LAI time-series. The LAI time-series were assimilated in the LINTUL model and validated for a second experimental field on which potatoes were grown in 2011. Yield in 2011 was simulated with an R2 of 0.82 when compared with field measured yield. Furthermore, we analysed the potential of assimilation of LAI into the LINTUL-3 model through the 'updating' assimilation technique. The deviation between measured and simulated yield decreased from 9371 kg/ha to 8729 kg/ha when assimilating weekly LAI measurements in the LINTUL model over the season of 2011. LINTUL-3 furthermore shows the main growth reducing factors, which are useful for farm decision support. The combination of crop models and sensor techniques shows promising results for precision agriculture application and thereby for reduction of the footprint agriculture has on the world's resources.

Hydrologic Remote Sensing and Land Surface Data Assimilation.

PubMed

Moradkhani, Hamid

2008-05-06

Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface-atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF) and Particle filter (PF), for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law) and could be a strong alternative to EnKF which is subject to some limitations including the linear updating rule and assumption of jointly normal distribution of errors in state variables and observation.

Spatially Distributed Assimilation of Remotely Sensed Leaf Area Index and Potential Evapotranspiration for Hydrologic Modeling in Wetland Landscapes

NASA Astrophysics Data System (ADS)

Rajib, A.; Evenson, G. R.; Golden, H. E.; Lane, C.

2017-12-01

Evapotranspiration (ET), a highly dynamic flux in wetland landscapes, regulates the accuracy of surface/sub-surface runoff simulation in a hydrologic model. Accordingly, considerable uncertainty in simulating ET-related processes remains, including our limited ability to incorporate realistic ground conditions, particularly those involved with complex land-atmosphere feedbacks, vegetation growth, and energy balances. Uncertainty persists despite using high resolution topography and/or detailed land use data. Thus, a good hydrologic model can produce right answers for wrong reasons. In this study, we develop an efficient approach for multi-variable assimilation of remotely sensed earth observations (EOs) into a hydrologic model and apply it in the 1700 km2 Pipestem Creek watershed in the Prairie Pothole Region of North Dakota, USA. Our goal is to employ EOs, specifically Leaf Area Index (LAI) and Potential Evapotranspiration (PET), as surrogates for the aforementioned processes without overruling the model's built-in physical/semi-empirical process conceptualizations. To do this, we modified the source code of an already-improved version of the Soil and Water Assessment Tool (SWAT) for wetland hydrology (Evenson et al. 2016 HP 30(22):4168) to directly assimilate remotely-sensed LAI and PET (obtained from the 500 m and 1 km Moderate Resolution Imaging Spectroradiometer (MODIS) gridded products, respectively) into each model Hydrologic Response Unit (HRU). Two configurations of the model, one with and one without EO assimilation, are calibrated against streamflow observations at the watershed outlet. Spatio-temporal changes in the HRU-level water balance, based on calibrated outputs, are evaluated using MODIS Actual Evapotranspiration (AET) as a reference. It is expected that the model configuration having remotely sensed LAI and PET, will simulate more realistic land-atmosphere feedbacks, vegetation growth and energy balance. As a result, this will decrease simulated water balance uncertainties compared to the default model configuration.

The suitability of remotely sensed soil moisture for improving operational flood forecasting

NASA Astrophysics Data System (ADS)

Wanders, N.; Karssenberg, D.; de Roo, A.; de Jong, S. M.; Bierkens, M. F. P.

2013-11-01

We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model for flood predictions with lead times up to 10 days. For this study, satellite-derived soil moisture from ASCAT, AMSR-E and SMOS is assimilated into the EFAS system for the Upper Danube basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into EFAS, an Ensemble Kalman Filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure optimal performance of the EnKF. For the validation, additional discharge observations not used in the EnKF, are used as an independent validation dataset. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the Mean Absolute Error (MAE) of the ensemble mean is reduced by 65%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of base flows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the Continuous Ranked Probability Score (CRPS) shows a performance increase of 5-10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more data is assimilated into the system and the best performance is achieved with the assimilation of both discharge and satellite observations. The additional gain is highest when discharge observations from both upstream and downstream areas are used in combination with the soil moisture data. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.

The suitability of remotely sensed soil moisture for improving operational flood forecasting

NASA Astrophysics Data System (ADS)

Wanders, N.; Karssenberg, D.; de Roo, A.; de Jong, S. M.; Bierkens, M. F. P.

2014-06-01

We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model (LISFLOOD) for flood predictions with lead times of up to 10 days. For this study, satellite-derived soil moisture from ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer - Earth Observing System) and SMOS (Soil Moisture and Ocean Salinity) is assimilated into the LISFLOOD model for the Upper Danube Basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into the hydrological model, an ensemble Kalman filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure increased performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation data set. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the mean absolute error (MAE) of the ensemble mean is reduced by 35%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of baseflows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the continuous ranked probability score (CRPS) shows a performance increase of 5-10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more observational data is assimilated into the system. The added values of the satellite data is largest when these observations are assimilated in combination with distributed discharge observations. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.

Remote sensing data assimilation for a prognostic phenology model

Treesearch

R. Stockli; T. Rutishauser; D. Dragoni; J. O' Keefe; P. E. Thornton; M. Jolly; L. Lu; A. S. Denning

2008-01-01

Predicting the global carbon and water cycle requires a realistic representation of vegetation phenology in climate models. However most prognostic phenology models are not yet suited for global applications, and diagnostic satellite data can be uncertain and lack predictive power. We present a framework for data assimilation of Fraction of Photosynthetically Active...

Assimilating Remotely Sensed Surface Soil Moisture into SWAT using Ensemble Kalman Filter

USDA-ARS?s Scientific Manuscript database

In this study, a 1-D Ensemble Kalman Filter has been used to update the soil moisture states of the Soil and Water Assessment Tool (SWAT) model. Experiments were conducted for the Cobb Creek Watershed in southeastern Oklahoma for 2006-2008. Assimilation of in situ data proved limited success in the ...

Assimilating Remote Sensing Observations of Leaf Area Index and Soil Moisture for Wheat Yield Estimates: An Observing System Simulation Experiment

NASA Technical Reports Server (NTRS)

Nearing, Grey S.; Crow, Wade T.; Thorp, Kelly R.; Moran, Mary S.; Reichle, Rolf H.; Gupta, Hoshin V.

2012-01-01

Observing system simulation experiments were used to investigate ensemble Bayesian state updating data assimilation of observations of leaf area index (LAI) and soil moisture (theta) for the purpose of improving single-season wheat yield estimates with the Decision Support System for Agrotechnology Transfer (DSSAT) CropSim-Ceres model. Assimilation was conducted in an energy-limited environment and a water-limited environment. Modeling uncertainty was prescribed to weather inputs, soil parameters and initial conditions, and cultivar parameters and through perturbations to model state transition equations. The ensemble Kalman filter and the sequential importance resampling filter were tested for the ability to attenuate effects of these types of uncertainty on yield estimates. LAI and theta observations were synthesized according to characteristics of existing remote sensing data, and effects of observation error were tested. Results indicate that the potential for assimilation to improve end-of-season yield estimates is low. Limitations are due to a lack of root zone soil moisture information, error in LAI observations, and a lack of correlation between leaf and grain growth.

Development of a WRF-RTFDDA-based high-resolution hybrid data-assimilation and forecasting system toward to operation in the Middle East

NASA Astrophysics Data System (ADS)

Liu, Y.; Wu, W.; Zhang, Y.; Kucera, P. A.; Liu, Y.; Pan, L.

2012-12-01

Weather forecasting in the Middle East is challenging because of its complicated geographical nature including massive coastal area and heterogeneous land, and regional spare observational network. Strong air-land-sea interactions form multi-scale weather regimes in the area, which require a numerical weather prediction model capable of properly representing multi-scale atmospheric flow with appropriate initial conditions. The WRF-based Real-Time Four Dimensional Data Assimilation (RTFDDA) system is one of advanced multi-scale weather analysis and forecasting facilities developed at the Research Applications Laboratory (RAL) of NCAR. The forecasting system is applied for the Middle East with careful configuration. To overcome the limitation of the very sparsely available conventional observations in the region, we develop a hybrid data assimilation algorithm combining RTFDDA and WRF-3DVAR, which ingests remote sensing data from satellites and radar. This hybrid data assimilation blends Newtonian nudging FDDA and 3DVAR technology to effectively assimilate both conventional observations and remote sensing measurements and provide improved initial conditions for the forecasting system. For brevity, the forecasting system is called RTF3H (RTFDDA-3DVAR Hybrid). In this presentation, we will discuss the hybrid data assimilation algorithm, and its implementation, and the applications for high-impact weather events in the area. Sensitivity studies are conducted to understand the strength and limitations of this hybrid data assimilation algorithm.

Improved Prediction of Quasi-Global Vegetation Conditions Using Remotely-Sensed Surface Soil Moisture

NASA Technical Reports Server (NTRS)

Bolten, John; Crow, Wade

2012-01-01

The added value of satellite-based surface soil moisture retrievals for agricultural drought monitoring is assessed by calculating the lagged rank correlation between remotely-sensed vegetation indices (VI) and soil moisture estimates obtained both before and after the assimilation of surface soil moisture retrievals derived from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) into a soil water balance model. Higher soil moisture/VI lag correlations imply an enhanced ability to predict future vegetation conditions using estimates of current soil moisture. Results demonstrate that the assimilation of AMSR-E surface soil moisture retrievals substantially improve the performance of a global drought monitoring system - particularly in sparsely-instrumented areas of the world where high-quality rainfall observations are unavailable.

Data Assimilation Results from PLASMON

NASA Astrophysics Data System (ADS)

Jorgensen, A. M.; Lichtenberger, J.; Duffy, J.; Friedel, R. H.; Clilverd, M.; Heilig, B.; Vellante, M.; Manninen, J. K.; Raita, T.; Rodger, C. J.; Collier, A.; Reda, J.; Holzworth, R. H.; Ober, D. M.; Boudouridis, A.; Zesta, E.; Chi, P. J.

2013-12-01

VLF and magnetometer observations can be used to remotely sense the plasmasphere. VLF whistler waves can be used to measure the electron density and magnetic Field Line Resonance (FLR) measurements can be used to measure the mass density. In principle it is then possible to remotely map the plasmasphere with a network of ground-based stations which are also less expensive and more permanent than satellites. The PLASMON project, funded by the EU FP-7 program, is in the process of doing just this. A large number of ground-based observations will be input into a data assimilative framework which models the plasmasphere structure and dynamics. The data assimilation framework combines the Ensemble Kalman Filter with the Dynamic Global Core Plasma Model. In this presentation we will describe the plasmasphere model, the data assimilation approach that we have taken, PLASMON data and data assimilation results for specific events.

Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation

USDA-ARS?s Scientific Manuscript database

The scale mismatch between remotely sensed observations and crop growth models simulated state variables decreases the reliability of crop yield estimates. To overcome this problem, we used a two-step data assimilation phases: first we generated a complete leaf area index (LAI) time series by combin...

Information content of in situ and remotely sensed chlorophyll-a: Learning from size-structured phytoplankton model

NASA Astrophysics Data System (ADS)

Laiolo, Leonardo; Matear, Richard; Baird, Mark E.; Soja-Woźniak, Monika; Doblin, Martina A.

2018-07-01

Chlorophyll-a measurements in the form of in situ observations and satellite ocean colour products are commonly used in data assimilation to calibrate marine biogeochemical models. Here, a two size-class phytoplankton biogeochemical model, with a 0D configuration, was used to simulate the surface chlorophyll-a dynamics (simulated surface Chl-a) for cyclonic and anticyclonic eddies off East Australia. An optical model was then used to calculate the inherent optical properties from the simulation and convert them into remote-sensing reflectance (Rrs). Subsequently, Rrs was used to produce a satellite-like estimate of the simulated surface Chl-a concentrations through the MODIS OC3M algorithm (simulated OC3M Chl-a). Identical parameter optimisation experiments were performed through the assimilation of the two separate datasets (simulated surface Chl-a and simulated OC3M Chl-a), with the purpose of investigating the contrasting information content of simulated surface Chl-a and remotely-sensed data sources. The results we present are based on the analysis of the distribution of a cost function, varying four parameters of the biogeochemical model. In our idealized experiments the simulated OC3M Chl-a product is a poor proxy for the total simulated surface Chl-a concentration. Furthermore, our result show the OC3M algorithm can underestimate the simulated chlorophyll-a concentration in offshore eddies off East Australia (Case I waters), because of the weak relationship between large-sized phytoplankton and remote-sensing reflectance. Although Case I waters are usually characteristic of oligotrophic environments, with a photosynthetic community typically represented by relatively small-sized phytoplankton, mesoscale features such as eddies can generate seasonally favourable conditions for a photosynthetic community with a greater proportion of large phytoplankton cells. Furthermore, our results show that in mesoscale features such as eddies, in situ chlorophyll-a observations and the ocean colour products can carry different information related to phytoplankton sizes. Assimilating both remote-sensing reflectance and measurements of in situ chlorophyll-a concentration reduces the uncertainty of the parameter values more than either data set alone, thus reducing the spread of acceptable solutions, giving an improved simulation of the natural environment.

Estimation de la superficie du couvert nival a partir d'une combinaison des donnees de teledetection MODIS et AMSR-E dans un contexte de prevision des crues printanieres au Quebec

NASA Astrophysics Data System (ADS)

Bergeron, Jean

Snow cover estimation is a principal source of error for spring streamflow simulations in Québec, Canada. Optical and near infrared remote sensing can improve snow cover area (SCA) estimation due to high spatial resolution but is limited by cloud cover and incoming solar radiation. Passive microwave remote sensing is complementary by its near-transparence to cloud cover and independence to incoming solar radiation, but is limited by its coarse spatial resolution. The study aims to create an improved SCA product from blended passive microwave (AMSR-E daily L3 Brightness Temperature) and optical (MODIS Terra and Aqua daily snow cover L3) remote sensing data in order to improve estimation of river streamflow caused by snowmelt with Québec's operational MOHYSE hydrological model through direct-insertion of the blended SCA product in a coupled snowmelt module (SPH-AV). SCA estimated from AMSR-E data is first compared with SCA estimated with MODIS, as well as with in situ snow depth measurements. Results show good agreement (+95%) between AMSR-E-derived and MODIS-derived SCA products in spring but comparisons with Environment Canada ground stations and SCA derived from Advanced Very High Resolution Radiometer (AVHRR) data show lesser agreements (83 % and 74% respectively). Results also show that AMSR-E generally underestimates SCA. Assimilating the blended snow product in SPH-AV coupled with MOHYSE yields significant improvement of simulated streamflow for the aux Écorces et au Saumon rivers overall when compared with simulations with no update during thaw events, These improvements are similar to results driven by biweekly ground data. Assimilation of remotely-sensed passive microwave data was also found to have little positive impact on springflood forecast due to the difficulty in differentiating melting snow from snow-free surfaces. Considering the direct-insertion and Newtonian nudging assimilation methods, the study also shows the latter method to be superior to the former, notably when assimilating noisy data. Keywords: Snow cover, spring streamflow, MODIS, AMSR-E, hydrological model.

Integrating remotely sensed surface water extent into continental scale hydrology

NASA Astrophysics Data System (ADS)

Revilla-Romero, Beatriz; Wanders, Niko; Burek, Peter; Salamon, Peter; de Roo, Ad

2016-12-01

In hydrological forecasting, data assimilation techniques are employed to improve estimates of initial conditions to update incorrect model states with observational data. However, the limited availability of continuous and up-to-date ground streamflow data is one of the main constraints for large-scale flood forecasting models. This is the first study that assess the impact of assimilating daily remotely sensed surface water extent at a 0.1° × 0.1° spatial resolution derived from the Global Flood Detection System (GFDS) into a global rainfall-runoff including large ungauged areas at the continental spatial scale in Africa and South America. Surface water extent is observed using a range of passive microwave remote sensors. The methodology uses the brightness temperature as water bodies have a lower emissivity. In a time series, the satellite signal is expected to vary with changes in water surface, and anomalies can be correlated with flood events. The Ensemble Kalman Filter (EnKF) is a Monte-Carlo implementation of data assimilation and used here by applying random sampling perturbations to the precipitation inputs to account for uncertainty obtaining ensemble streamflow simulations from the LISFLOOD model. Results of the updated streamflow simulation are compared to baseline simulations, without assimilation of the satellite-derived surface water extent. Validation is done in over 100 in situ river gauges using daily streamflow observations in the African and South American continent over a one year period. Some of the more commonly used metrics in hydrology were calculated: KGE', NSE, PBIAS%, R2, RMSE, and VE. Results show that, for example, NSE score improved on 61 out of 101 stations obtaining significant improvements in both the timing and volume of the flow peaks. Whereas the validation at gauges located in lowland jungle obtained poorest performance mainly due to the closed forest influence on the satellite signal retrieval. The conclusion is that remotely sensed surface water extent holds potential for improving rainfall-runoff streamflow simulations, potentially leading to a better forecast of the peak flow.

Hyperspectral Remote Sensing of the Coastal Ocean: Adaptive Sampling and Forecasting of In situ Optical Properties

DTIC Science & Technology

2003-09-30

We are developing an integrated rapid environmental assessment capability that will be used to feed an ocean nowcast/forecast system. The goal is to develop a capacity for predicting the dynamics in inherent optical properties in coastal waters. This is being accomplished by developing an integrated observation system that is being coupled to a data assimilative hydrodynamic bio-optical ecosystem model. The system was used adaptively to calibrate hyperspectral remote sensing sensors in optically complex nearshore coastal waters.

Perspectives in remote sensing in Brazil. An approach of the remote sensing applications to Earth resources surveys

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Novaes, R. A.

1982-01-01

Since the systematic use of earth surface data collection by orbital sensor systems started in 1972 with the launching of the North American LANDSAT satellite, a great effort has been made to assimilate, develop and transfer remote sensing technology (data acquisition and analysis) in its many applications in Brazil. The availability of sensor systems and existing data is considered approached, as well as those which will soon be available to the Brazilian researchers. The new systems of the LANDSAT-4, of the Columbia space shuttle and of the French satellites of the SPOT series are discussed. Some characteristics of the sensor system for the first Brazilian remote sensing satellite, to be launched by the end of the decade, are presented. Some LANDSAT-4 and SPOT simulation products are shown, emphasizing how the data obtained by these new satellites can be applied.

Modeling Atmospheric CO2 Processes to Constrain the Missing Sink

NASA Technical Reports Server (NTRS)

Kawa, S. R.; Denning, A. S.; Erickson, D. J.; Collatz, J. C.; Pawson, S.

2005-01-01

We report on a NASA supported modeling effort to reduce uncertainty in carbon cycle processes that create the so-called missing sink of atmospheric CO2. Our overall objective is to improve characterization of CO2 source/sink processes globally with improved formulations for atmospheric transport, terrestrial uptake and release, biomass and fossil fuel burning, and observational data analysis. The motivation for this study follows from the perspective that progress in determining CO2 sources and sinks beyond the current state of the art will rely on utilization of more extensive and intensive CO2 and related observations including those from satellite remote sensing. The major components of this effort are: 1) Continued development of the chemistry and transport model using analyzed meteorological fields from the Goddard Global Modeling and Assimilation Office, with comparison to real time data in both forward and inverse modes; 2) An advanced biosphere model, constrained by remote sensing data, coupled to the global transport model to produce distributions of CO2 fluxes and concentrations that are consistent with actual meteorological variability; 3) Improved remote sensing estimates for biomass burning emission fluxes to better characterize interannual variability in the atmospheric CO2 budget and to better constrain the land use change source; 4) Evaluating the impact of temporally resolved fossil fuel emission distributions on atmospheric CO2 gradients and variability. 5) Testing the impact of existing and planned remote sensing data sources (e.g., AIRS, MODIS, OCO) on inference of CO2 sources and sinks, and use the model to help establish measurement requirements for future remote sensing instruments. The results will help to prepare for the use of OCO and other satellite data in a multi-disciplinary carbon data assimilation system for analysis and prediction of carbon cycle changes and carbodclimate interactions.

Estimation of snow emissivity via assimilation of multi-frequency passive microwave data into an ensemble-based data assimilation system

NASA Astrophysics Data System (ADS)

Farhadi, L.; Bateni, S. M.; Auligne, T.; Navari, M.

2017-12-01

Snow emissivity is a key parameter for the estimation of snow surface temperature, which is needed as an initial value in climate models and determination of the outgoing long-wave radiation. Moreover, snow emissivity is required for retrieval of atmospheric parameters (e.g., temperature and humidity profiles) from satellite measurements and satellite data assimilations in numerical weather prediction systems. Microwave emission models and remote sensing data cannot accurately estimate snow emissivity due to limitations attributed to each of them. Existing microwave emission models introduce significant uncertainties in their snow emissivity estimates. This is mainly due to shortcomings of the dense media theory for snow medium at high frequencies, and erroneous forcing variables. The well-known limitations of passive microwave data such as coarse spatial resolution, saturation in deep snowpack, and signal loss in wet snow are the major drawbacks of passive microwave retrieval algorithms for estimation of snow emissivity. A full exploitation of the information contained in the remote sensing data can be achieved by merging them with snow emission models within a data assimilation framework. Such an optimal merging can overcome the specific limitations of models and remote sensing data. An Ensemble Batch Smoother (EnBS) data assimilation framework was developed in this study to combine the synthetically generated passive microwave brightness temperatures at 1.4-, 18.7-, 36.5-, and 89-GHz frequencies with the MEMLS microwave emission model to reduce the uncertainty of the snow emissivity estimates. We have used the EnBS algorithm in the context of observing system simulation experiment (or synthetic experiment) at the local scale observation site (LSOS) of the NASA CLPX field campaign. Our findings showed that the developed methodology significantly improves the estimates of the snow emissivity. The simultaneous assimilation of passive microwave brightness temperatures at all frequencies (i.e., 1.4-, 18.7-, 36.5-, and 89-GHz) reduce the root-mean-square-error (RMSE) of snow emissivity at 1.4-, 18.7-, 36.5-, and 89-GHz (H-pol.) by 80%, 42%, 52%, 40%, respectively compared to the corresponding snow emissivity estimates from the open-loop model.

On the assimilation set-up of ASCAT soil moisture data for improving streamflow catchment simulation

NASA Astrophysics Data System (ADS)

Loizu, Javier; Massari, Christian; Álvarez-Mozos, Jesús; Tarpanelli, Angelica; Brocca, Luca; Casalí, Javier

2018-01-01

Assimilation of remotely sensed surface soil moisture (SSM) data into hydrological catchment models has been identified as a means to improve streamflow simulations, but reported results vary markedly depending on the particular model, catchment and assimilation procedure used. In this study, the influence of key aspects, such as the type of model, re-scaling technique and SSM observation error considered, were evaluated. For this aim, Advanced SCATterometer ASCAT-SSM observations were assimilated through the ensemble Kalman filter into two hydrological models of different complexity (namely MISDc and TOPLATS) run on two Mediterranean catchments of similar size (750 km2). Three different re-scaling techniques were evaluated (linear re-scaling, variance matching and cumulative distribution function matching), and SSM observation error values ranging from 0.01% to 20% were considered. Four different efficiency measures were used for evaluating the results. Increases in Nash-Sutcliffe efficiency (0.03-0.15) and efficiency indices (10-45%) were obtained, especially when linear re-scaling and observation errors within 4-6% were considered. This study found out that there is a potential to improve streamflow prediction through data assimilation of remotely sensed SSM in catchments of different characteristics and with hydrological models of different conceptualizations schemes, but for that, a careful evaluation of the observation error and re-scaling technique set-up utilized is required.

Farmland Drought Evaluation Based on the Assimilation of Multi-Temporal Multi-Source Remote Sensing Data into AquaCrop Model

NASA Astrophysics Data System (ADS)

Yang, Guijun; Yang, Hao; Jin, Xiuliang; Pignatti, Stefano; Casa, Faffaele; Silverstro, Paolo Cosmo

2016-08-01

Drought is the most costly natural disasters in China and all over the world. It is very important to evaluate the drought-induced crop yield losses and further improve water use efficiency at regional scale. Firstly, crop biomass was estimated by the combined use of Synthetic Aperture Radar (SAR) and optical remote sensing data. Then the estimated biophysical variable was assimilated into crop growth model (FAO AquaCrop) by the Particle Swarm Optimization (PSO) method from farmland scale to regional scale.At farmland scale, the most important crop parameters of AquaCrop model were determined to reduce the used parameters in assimilation procedure. The Extended Fourier Amplitude Sensitivity Test (EFAST) method was used for assessing the contribution of different crop parameters to model output. Moreover, the AquaCrop model was calibrated using the experiment data in Xiaotangshan, Beijing.At regional scale, spatial application of our methods were carried out and validated in the rural area of Yangling, Shaanxi Province, in 2014. This study will provide guideline to make irrigation decision of balancing of water consumption and yield loss.

Soil moisture assimilation using a modified ensemble transform Kalman filter with water balance constraint

NASA Astrophysics Data System (ADS)

Wu, Guocan; Zheng, Xiaogu; Dan, Bo

2016-04-01

The shallow soil moisture observations are assimilated into Common Land Model (CoLM) to estimate the soil moisture in different layers. The forecast error is inflated to improve the analysis state accuracy and the water balance constraint is adopted to reduce the water budget residual in the assimilation procedure. The experiment results illustrate that the adaptive forecast error inflation can reduce the analysis error, while the proper inflation layer can be selected based on the -2log-likelihood function of the innovation statistic. The water balance constraint can result in reducing water budget residual substantially, at a low cost of assimilation accuracy loss. The assimilation scheme can be potentially applied to assimilate the remote sensing data.

Winter wheat yield estimation of remote sensing research based on WOFOST crop model and leaf area index assimilation

NASA Astrophysics Data System (ADS)

Chen, Yanling; Gong, Adu; Li, Jing; Wang, Jingmei

2017-04-01

Accurate crop growth monitoring and yield predictive information are significant to improve the sustainable development of agriculture and ensure the security of national food. Remote sensing observation and crop growth simulation models are two new technologies, which have highly potential applications in crop growth monitoring and yield forecasting in recent years. However, both of them have limitations in mechanism or regional application respectively. Remote sensing information can not reveal crop growth and development, inner mechanism of yield formation and the affection of environmental meteorological conditions. Crop growth simulation models have difficulties in obtaining data and parameterization from single-point to regional application. In order to make good use of the advantages of these two technologies, the coupling technique of remote sensing information and crop growth simulation models has been studied. Filtering and optimizing model parameters are key to yield estimation by remote sensing and crop model based on regional crop assimilation. Winter wheat of GaoCheng was selected as the experiment object in this paper. And then the essential data was collected, such as biochemical data and farmland environmental data and meteorological data about several critical growing periods. Meanwhile, the image of environmental mitigation small satellite HJ-CCD was obtained. In this paper, research work and major conclusions are as follows. (1) Seven vegetation indexes were selected to retrieve LAI, and then linear regression model was built up between each of these indexes and the measured LAI. The result shows that the accuracy of EVI model was the highest (R2=0.964 at anthesis stage and R2=0.920 at filling stage). Thus, EVI as the most optimal vegetation index to predict LAI in this paper. (2) EFAST method was adopted in this paper to conduct the sensitive analysis to the 26 initial parameters of the WOFOST model and then a sensitivity index was constructed to evaluate the influence of each parameter mentioned above on the winter wheat yield formation. Finally, six parameters that sensitivity index more than 0.1 as sensitivity factors were chose, which are TSUM1, SLATB1, SLATB2, SPAN, EFFTB3 and TMPF4. To other parameters, we confirmed them via practical measurement and calculation, available literature or WOFOST default. Eventually, we completed the regulation of WOFOST parameters. (3) Look-up table algorithm was used to realize single-point yield estimation through the assimilation of the WOFOST model and the retrieval LAI. This simulation achieved a high accuracy which perfectly meet the purpose of assimilation (R2=0.941 and RMSE=194.58kg/hm2). In this paper, the optimum value of sensitivity parameters were confirmed and the estimation of single-point yield were finished. Key words: yield estimation of winter wheat, LAI, WOFOST crop growth model, assimilation

Assimilation of repeated woody biomass observations constrains decadal ecosystem carbon cycle uncertainty in aggrading forests

NASA Astrophysics Data System (ADS)

Smallman, T. L.; Exbrayat, J.-F.; Mencuccini, M.; Bloom, A. A.; Williams, M.

2017-03-01

Forest carbon sink strengths are governed by plant growth, mineralization of dead organic matter, and disturbance. Across landscapes, remote sensing can provide information about aboveground states of forests and this information can be linked to models to estimate carbon cycling in forests close to steady state. For aggrading forests this approach is more challenging and has not been demonstrated. Here we apply a Bayesian approach, linking a simple model to a range of data, to evaluate their information content, for two aggrading forests. We compare high information content analyses using local observations with retrievals using progressively sparser remotely sensed information (repeated, single, and no woody biomass observations). The net biome productivity of both forests is constrained to be a net sink with <2 Mg C ha-1 yr-1 variation across the range of inputs. However, the sequestration of particular carbon pool(s) varies with assimilated biomass information. Assimilation of repeated biomass observations reduces uncertainty and/or bias in all ecosystem C pools not just wood, compared to analyses using single or no stock information. As verification, our repeated biomass analysis explains 78-86% of variation in litter dynamics at one forest, while at the second forest total dead organic matter estimates are within observational uncertainty. The uncertainty of retrieved ecosystem traits in the repeated biomass analysis is reduced by up to 50% compared to analyses with less biomass information. This study quantifies the importance of repeated woody observations in constraining the dynamics of both wood and dead organic matter, highlighting the benefit of proposed remote sensing missions.

A Note on NCOM Temperature Forecast Error Calibration Using the Ensemble Transform

DTIC Science & Technology

2009-01-01

Division Head Ruth H. Preller, 7300 Security, Code 1226 Office of Counsel,Code 1008.3 ADOR/Director NCST E. R. Franchi , 7000 Public Affairs...problem, local unbiased (correlation) and persistent errors (bias) of the Navy Coastal Ocean Modeling (NCOM) System nested in global ocean domains, are...system were made available in real-time without performing local data assimilation, though remote sensing and global data was assimilated on the

Hyperspectral Thermal Infrared Remote Sensing of the Land Surface and Target Identification using Airborne Interferometry

DTIC Science & Technology

2009-10-01

variational data assimilation technique are profiles of temperature, water vapour and ozone , surface temperature and spectrally varying emissivity. HOW TO...that are insensitive to the land surface because of the complexity of the land surface emissivity. We have utilised the techniques described here for...state as well as surface properties. Furthermore with by utilising a variational assimilation technique and a state of the art Numerical Weather

Towards improved hydrologic predictions using data assimilation techniques for water resource management at the continental scale

NASA Astrophysics Data System (ADS)

Naz, Bibi; Kurtz, Wolfgang; Kollet, Stefan; Hendricks Franssen, Harrie-Jan; Sharples, Wendy; Görgen, Klaus; Keune, Jessica; Kulkarni, Ketan

2017-04-01

More accurate and reliable hydrologic simulations are important for many applications such as water resource management, future water availability projections and predictions of extreme events. However, simulation of spatial and temporal variations in the critical water budget components such as precipitation, snow, evaporation and runoff is highly uncertain, due to errors in e.g. model structure and inputs (hydrologic parameters and forcings). In this study, we use data assimilation techniques to improve the predictability of continental-scale water fluxes using in-situ measurements along with remotely sensed information to improve hydrologic predications for water resource systems. The Community Land Model, version 3.5 (CLM) integrated with the Parallel Data Assimilation Framework (PDAF) was implemented at spatial resolution of 1/36 degree (3 km) over the European CORDEX domain. The modeling system was forced with a high-resolution reanalysis system COSMO-REA6 from Hans-Ertel Centre for Weather Research (HErZ) and ERA-Interim datasets for time period of 1994-2014. A series of data assimilation experiments were conducted to assess the efficiency of assimilation of various observations, such as river discharge data, remotely sensed soil moisture, terrestrial water storage and snow measurements into the CLM-PDAF at regional to continental scales. This setup not only allows to quantify uncertainties, but also improves streamflow predictions by updating simultaneously model states and parameters utilizing observational information. The results from different regions, watershed sizes, spatial resolutions and timescales are compared and discussed in this study.

Integrating remotely sensed surface water extent into continental scale hydrology.

PubMed

Revilla-Romero, Beatriz; Wanders, Niko; Burek, Peter; Salamon, Peter; de Roo, Ad

2016-12-01

In hydrological forecasting, data assimilation techniques are employed to improve estimates of initial conditions to update incorrect model states with observational data. However, the limited availability of continuous and up-to-date ground streamflow data is one of the main constraints for large-scale flood forecasting models. This is the first study that assess the impact of assimilating daily remotely sensed surface water extent at a 0.1° × 0.1° spatial resolution derived from the Global Flood Detection System (GFDS) into a global rainfall-runoff including large ungauged areas at the continental spatial scale in Africa and South America. Surface water extent is observed using a range of passive microwave remote sensors. The methodology uses the brightness temperature as water bodies have a lower emissivity. In a time series, the satellite signal is expected to vary with changes in water surface, and anomalies can be correlated with flood events. The Ensemble Kalman Filter (EnKF) is a Monte-Carlo implementation of data assimilation and used here by applying random sampling perturbations to the precipitation inputs to account for uncertainty obtaining ensemble streamflow simulations from the LISFLOOD model. Results of the updated streamflow simulation are compared to baseline simulations, without assimilation of the satellite-derived surface water extent. Validation is done in over 100 in situ river gauges using daily streamflow observations in the African and South American continent over a one year period. Some of the more commonly used metrics in hydrology were calculated: KGE', NSE, PBIAS%, R 2 , RMSE, and VE. Results show that, for example, NSE score improved on 61 out of 101 stations obtaining significant improvements in both the timing and volume of the flow peaks. Whereas the validation at gauges located in lowland jungle obtained poorest performance mainly due to the closed forest influence on the satellite signal retrieval. The conclusion is that remotely sensed surface water extent holds potential for improving rainfall-runoff streamflow simulations, potentially leading to a better forecast of the peak flow.

Assimilation of remote sensing data into a process-based ecosystem model for monitoring changes of soil water content in croplands

NASA Astrophysics Data System (ADS)

Ju, Weimin; Gao, Ping; Wang, Jun; Li, Xianfeng; Chen, Shu

2008-10-01

Soil water content (SWC) is an important factor affecting photosynthesis, growth, and final yields of crops. The information on SWC is of importance for mitigating the reduction of crop yields caused by drought through proper agricultural water management. A variety of methodologies have been developed to estimate SWC at local and regional scales, including field sampling, remote sensing monitoring and model simulations. The reliability of regional SWC simulation depends largely on the accuracy of spatial input datasets, including vegetation parameters, soil and meteorological data. Remote sensing has been proved to be an effective technique for controlling uncertainties in vegetation parameters. In this study, the vegetation parameters (leaf area index and land cover type) derived from the Moderate Resolution Imaging Spectrometer (MODIS) were assimilated into a process-based ecosystem model BEPS for simulating the variations of SWC in croplands of Jiangsu province, China. Validation shows that the BEPS model is able to capture 81% and 83% of across-site variations of SWC at 10 and 20 cm depths during the period from September to December, 2006 when a serous autumn drought occurred. The simulated SWC responded the events of rainfall well at regional scale, demonstrating the usefulness of our methodology for SWC and practical agricultural water management at large scales.

A remote sensing data assimilation system for cold land processes hydrologic estimation

NASA Astrophysics Data System (ADS)

Andreadis, Konstantinos M.

2009-12-01

Accurate forecasting of snow properties is important for effective water resources management, especially in mountainous areas. Model-based approaches are limited by biases and uncertainties. Remote sensing offers an opportunity for observation of snow properties over larger areas. Traditional approaches to direct estimation of snow properties from passive microwave remote sensing have been plagued by limitations such as the tendency of estimates to saturate for moderately deep snowpacks and the effects of mixed land cover. To address these complications, a data assimilation system is developed and evaluated in a three-part research. The data assimilation system requires the embedding of a microwave emissions model which uses modeled snowpack properties. In the first part of this study, such a model is evaluated using multi-scale TB measurements from the Cold Land Processes Experiment. The model's ability to reproduce snowpack microphysical properties is evaluated through comparison with snowpit measurements, while TB predictions are evaluated through comparison with in-situ, aircraft and satellite measurements. Point comparisons showed limitations in the model, while the spatial averaging and the effects of forest cover suppressed errors in comparisons with aircraft measurements. The layered character of snowpacks increases the complexity of algorithms intended to retrieve snow properties from the snowpack microwave return signal. Implementation of a retrieval strategy requires knowledge of stratigraphy, which practically can only be produced by models. In the second part of this study, we describe a multi-layer model designed for such applications. The model coupled with a radiative transfer scheme improved the estimation of TB, while potential impacts when assimilating radiances are explored. A system that merges SWE model predictions and observations of SCE and TB, is evaluated in the third part of this study over one winter season in the Upper Snake River basin. Two data assimilation techniques, the Ensemble Kalman filter and the Ensemble Multiscale Kalman filter are tested with the multilayer snow model forced by downscaled re-analysis meteorological observations. Both the EnKF and EnMKF showed modest improvements when compared with the open-loop simulation, relative to a baseline simulation which used in-situ meteorological data, while comparisons with in-situ SWE measurements showed an overall improvement.

Progress in remote sensing of global land surface heat fluxes and evaporations with a turbulent heat exchange parameterization method

NASA Astrophysics Data System (ADS)

Chen, Xuelong; Su, Bob

2017-04-01

Remote sensing has provided us an opportunity to observe Earth land surface with a much higher resolution than any of GCM simulation. Due to scarcity of information for land surface physical parameters, up-to-date GCMs still have large uncertainties in the coupled land surface process modeling. One critical issue is a large amount of parameters used in their land surface models. Thus remote sensing of land surface spectral information can be used to provide information on these parameters or assimilated to decrease the model uncertainties. Satellite imager could observe the Earth land surface with optical, thermal and microwave bands. Some basic Earth land surface status (land surface temperature, canopy height, canopy leaf area index, soil moisture etc.) has been produced with remote sensing technique, which already help scientists understanding Earth land and atmosphere interaction more precisely. However, there are some challenges when applying remote sensing variables to calculate global land-air heat and water exchange fluxes. Firstly, a global turbulent exchange parameterization scheme needs to be developed and verified, especially for global momentum and heat roughness length calculation with remote sensing information. Secondly, a compromise needs to be innovated to overcome the spatial-temporal gaps in remote sensing variables to make the remote sensing based land surface fluxes applicable for GCM model verification or comparison. A flux network data library (more 200 flux towers) was collected to verify the designed method. Important progress in remote sensing of global land flux and evaporation will be presented and its benefits for GCM models will also be discussed. Some in-situ studies on the Tibetan Plateau and problems of land surface process simulation will also be discussed.

The Global Observing System in the Assimilation Context

NASA Technical Reports Server (NTRS)

Reinecker, Michele M.; Gelaro, R.; Pawson, S.; Reichle, R.; McCarty, W.

2011-01-01

Weather and climate analyses and predictions all rely on the global observing system. However, the observing system, whether atmosphere, ocean, or land surface, yields a diverse set of incomplete observations of the different components of Earth s environment. Data assimilation systems are essential to synthesize the wide diversity of in situ and remotely sensed observations into four-dimensional state estimates by combining the various observations with model-based estimates. Assimilation, or associated tools and products, are also useful in providing guidance for the evolution of the observing system of the future. This paper provides a brief overview of the global observing system and information gleaned through assimilation tools, and presents some evaluations of observing system gaps and issues.

Overview of Chinese GRAPES Data Assimilation System

NASA Astrophysics Data System (ADS)

Liu, Yan

2017-04-01

The development of data assimilation system of Global and Regional Assimilation and Prediction System (GRAPES in short) which is Chinese new generation operational numerical weather prediction system completed in recent years is reviewed in this paper, including the design scheme and main characteristics. GRAPES adopts the variational approach with stresses at application of various remote sensing observational data. Its development path is from three dimensional to four dimensional assimilation. It may be implemented with limited area or global configurations. The three dimensional variational data assimilation systems have been operational in the national and a few of regional meteorological centers. The global four dimensional assimilation system is in pre-operational experiments, and will be upgraded. After a brief introduction to the GRAPES data assimilation system, results of a series of validations of GRAPES analyses against the observation data and analyses derived from other operational NWP center to assess its performance are presented.

A review of the remote sensing of lower tropospheric thermodynamic profiles and its indispensable role for the understanding and the simulation of water and energy cycles: REMOTE SENSING OF THERMODYNAMIC PROFILES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wulfmeyer, Volker; Hardesty, R. Michael; Turner, David D.

A review of remote sensing technology for lower tropospheric thermodynamic (TD) profiling is presented with focus on high accuracy and high temporal-vertical resolution. The contributions of these instruments to the understanding of the Earth system are assessed with respect to radiative transfer, land surface-atmosphere feedback, convection initiation, and data assimilation. We demonstrate that for progress in weather and climate research, TD profilers are essential. These observational systems must resolve gradients of humidity and temperature in the stable or unstable atmospheric surface layer close to the ground, in the mixed layer, in the interfacial layer—usually characterized by an inversion—and the lowermore » troposphere. A thorough analysis of the current observing systems is performed revealing significant gaps that must be addressed to fulfill existing needs. We analyze whether current and future passive and active remote sensing systems can close these gaps. A methodological analysis and demonstration of measurement capabilities with respect to bias and precision is executed both for passive and active remote sensing including passive infrared and microwave spectroscopy, the global navigation satellite system, as well as water vapor and temperature Raman lidar and water vapor differential absorption lidar. Whereas passive remote sensing systems are already mature with respect to operational applications, active remote sensing systems require further engineering to become operational in networks. However, active remote sensing systems provide a smaller bias as well as higher temporal and vertical resolutions. For a suitable mesoscale network design, TD profiler system developments should be intensified and dedicated observing system simulation experiments should be performed.« less

Remote sensing validation through SOOP technology: implementation of Spectra system

NASA Astrophysics Data System (ADS)

Piermattei, Viviana; Madonia, Alice; Bonamano, Simone; Consalvi, Natalizia; Caligiore, Aurelio; Falcone, Daniela; Puri, Pio; Sarti, Fabio; Spaccavento, Giovanni; Lucarini, Diego; Pacci, Giacomo; Amitrano, Luigi; Iacullo, Salvatore; D'Andrea, Salvatore; Marcelli, Marco

2017-04-01

The development of low-cost instrumentation plays a key role in marine environmental studies and represents one of the most innovative aspects of marine research. The availability of low-cost technologies allows the realization of extended observatory networks for the study of marine phenomena through an integrated approach merging observations, remote sensing and operational oceanography. Marine services and practical applications critically depends on the availability of large amount of data collected with sufficiently dense spatial and temporal sampling. This issue directly influences the robustness both of ocean forecasting models and remote sensing observations through data assimilation and validation processes, particularly in the biological domain. For this reason it is necessary the development of cheap, small and integrated smart sensors, which could be functional both for satellite data validation and forecasting models data assimilation as well as to support early warning systems for environmental pollution control and prevention. This is particularly true in coastal areas, which are subjected to multiple anthropic pressures. Moreover, coastal waters can be classified like case 2 waters, where the optical properties of inorganic suspended matter and chromophoric dissolved organic matter must be considered and separated by the chlorophyll a contribution. Due to the high costs of mooring systems, research vessels, measure platforms and instrumentation a big effort was dedicated to the design, development and realization of a new low cost mini-FerryBox system: Spectra. Thanks to the modularity and user-friendly employment of the system, Spectra allows to acquire continuous in situ measures of temperature, conductivity, turbidity, chlorophyll a and chromophoric dissolved organic matter (CDOM) fluorescences from voluntary vessels, even by non specialized operators (Marcelli et al., 2014; 2016). This work shows the preliminary application of this technology to remote sensing data validation.

Sequential estimation and satellite data assimilation in meteorology and oceanography

NASA Technical Reports Server (NTRS)

Ghil, M.

1986-01-01

The role of dynamics in estimating the state of the atmosphere and ocean from incomplete and noisy data is discussed and the classical applications of four-dimensional data assimilation to large-scale atmospheric dynamics are presented. It is concluded that sequential updating of a forecast model with continuously incoming conventional and remote-sensing data is the most natural way of extracting the maximum amount of information from the imperfectly known dynamics, on the one hand, and the inaccurate and incomplete observations, on the other.

"One-Stop Shopping" for Ocean Remote-Sensing and Model Data

NASA Technical Reports Server (NTRS)

Li, P. Peggy; Vu, Quoc; Chao, Yi; Li, Zhi-Jin; Choi, Jei-Kook

2006-01-01

OurOcean Portal 2.0 (http:// ourocean.jpl.nasa.gov) is a software system designed to enable users to easily gain access to ocean observation data, both remote-sensing and in-situ, configure and run an Ocean Model with observation data assimilated on a remote computer, and visualize both the observation data and the model outputs. At present, the observation data and models focus on the California coastal regions and Prince William Sound in Alaska. This system can be used to perform both real-time and retrospective analyses of remote-sensing data and model outputs. OurOcean Portal 2.0 incorporates state-of-the-art information technologies (IT) such as MySQL database, Java Web Server (Apache/Tomcat), Live Access Server (LAS), interactive graphics with Java Applet at the Client site and MatLab/GMT at the server site, and distributed computing. OurOcean currently serves over 20 real-time or historical ocean data products. The data are served in pre-generated plots or their native data format. For some of the datasets, users can choose different plotting parameters and produce customized graphics. OurOcean also serves 3D Ocean Model outputs generated by ROMS (Regional Ocean Model System) using LAS. The Live Access Server (LAS) software, developed by the Pacific Marine Environmental Laboratory (PMEL) of the National Oceanic and Atmospheric Administration (NOAA), is a configurable Web-server program designed to provide flexible access to geo-referenced scientific data. The model output can be views as plots in horizontal slices, depth profiles or time sequences, or can be downloaded as raw data in different data formats, such as NetCDF, ASCII, Binary, etc. The interactive visualization is provided by graphic software, Ferret, also developed by PMEL. In addition, OurOcean allows users with minimal computing resources to configure and run an Ocean Model with data assimilation on a remote computer. Users may select the forcing input, the data to be assimilated, the simulation period, and the output variables and submit the model to run on a backend parallel computer. When the run is complete, the output will be added to the LAS server for

Foreword to the Special Issue on the 11th Specialist Meeting on Microwave Radiometry and Remote Sensing Applications (MicroRad 2010)

NASA Technical Reports Server (NTRS)

Le Vine, David M; Jackson, Thomas J.; Kim, Edward J.; Lang, Roger H.

2011-01-01

The Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad 2010) was held in Washington, DC from March 1 to 4, 2010. The objective of MicroRad 2010 was to provide an open forum to report and discuss recent advances in the field of microwave radiometry, particularly with application to remote sensing of the environment. The meeting was highly successful, with more than 200 registrations representing 48 countries. There were 80 oral presentations and more than 100 posters. MicroRad has become a venue for the microwave radiometry community to present new research results, instrument designs, and applications to an audience that is conversant in these issues. The meeting was divided into 16 sessions (listed in order of presentation): 1) SMOS Mission; 2) Future Passive Microwave Remote Sensing Missions; 3) Theory and Physical Principles of Electromagnetic Models; 4) Field Experiment Results; 5) Soil Moisture and Vegetation; 6) Snow and Cryosphere; 7) Passive/Active Microwave Remote Sensing Synergy; 8) Oceans; 9) Atmospheric Sounding and Assimilation; 10) Clouds and Precipitation; 11) Instruments and Advanced Techniques I; 12) Instruments and Advanced Techniques II; 13) Cross Calibration of Satellite Radiometers; 14) Calibration Theory and Methodology; 15) New Technologies for Microwave Radiometry; 16) Radio Frequency Interference.

Spatially Distributed Assimilation of Remotely Sensed Leaf Area Index and Potential Evapotranspiration for Hydrologic Modeling in Wetland Landscapes

EPA Science Inventory

Evapotranspiration (ET), a highly dynamic flux in wetland landscapes, regulates the accuracy of surface/sub-surface runoff simulation in a hydrologic model. However, considerable uncertainty in simulating ET-related processes remains, including our limited ability to incorporate ...

Improving root-zone soil moisture estimations using dynamic root growth and crop phenology

USDA-ARS?s Scientific Manuscript database

Water Energy Balance (WEB) Soil Vegetation Atmosphere Transfer (SVAT) modelling can be used to estimate soil moisture by forcing the model with observed data such as precipitation and solar radiation. Recently, an innovative approach that assimilates remotely sensed thermal infrared (TIR) observatio...

Benchmarking a soil moisture data assimilation system for agricultural drought monitoring

USDA-ARS?s Scientific Manuscript database

Agricultural drought is defined as a shortage of moisture in the root zone of plants. Recently available satellite-based remote sensing data have accelerated development of drought early warning system by providing spatially continuous soil moisture information repeatedly at short-term interval. Non...

Seasonal sea ice predictions for the Arctic based on assimilation of remotely sensed observations

NASA Astrophysics Data System (ADS)

Kauker, F.; Kaminski, T.; Ricker, R.; Toudal-Pedersen, L.; Dybkjaer, G.; Melsheimer, C.; Eastwood, S.; Sumata, H.; Karcher, M.; Gerdes, R.

2015-10-01

The recent thinning and shrinking of the Arctic sea ice cover has increased the interest in seasonal sea ice forecasts. Typical tools for such forecasts are numerical models of the coupled ocean sea ice system such as the North Atlantic/Arctic Ocean Sea Ice Model (NAOSIM). The model uses as input the initial state of the system and the atmospheric boundary condition over the forecasting period. This study investigates the potential of remotely sensed ice thickness observations in constraining the initial model state. For this purpose it employs a variational assimilation system around NAOSIM and the Alfred Wegener Institute's CryoSat-2 ice thickness product in conjunction with the University of Bremen's snow depth product and the OSI SAF ice concentration and sea surface temperature products. We investigate the skill of predictions of the summer ice conditions starting in March for three different years. Straightforward assimilation of the above combination of data streams results in slight improvements over some regions (especially in the Beaufort Sea) but degrades the over-all fit to independent observations. A considerable enhancement of forecast skill is demonstrated for a bias correction scheme for the CryoSat-2 ice thickness product that uses a spatially varying scaling factor.

Application of Terrestrial Microwave Remote Sensing to Agricultural Drought Monitoring

NASA Astrophysics Data System (ADS)

Crow, W. T.; Bolten, J. D.

2014-12-01

Root-zone soil moisture information is a valuable diagnostic for detecting the onset and severity of agricultural drought. Current attempts to globally monitor root-zone soil moisture are generally based on the application of soil water balance models driven by observed meteorological variables. Such systems, however, are prone to random error associated with: incorrect process model physics, poor parameter choices and noisy meteorological inputs. The presentation will describe attempts to remediate these sources of error via the assimilation of remotely-sensed surface soil moisture retrievals from satellite-based passive microwave sensors into a global soil water balance model. Results demonstrate the ability of satellite-based soil moisture retrieval products to significantly improve the global characterization of root-zone soil moisture - particularly in data-poor regions lacking adequate ground-based rain gage instrumentation. This success has lead to an on-going effort to implement an operational land data assimilation system at the United States Department of Agriculture's Foreign Agricultural Service (USDA FAS) to globally monitor variations in root-zone soil moisture availability via the integration of satellite-based precipitation and soil moisture information. Prospects for improving the performance of the USDA FAS system via the simultaneous assimilation of both passive and active-based soil moisture retrievals derived from the upcoming NASA Soil Moisture Active/Passive mission will also be discussed.

Data assimilation of citizen collected information for real-time flood hazard mapping

NASA Astrophysics Data System (ADS)

Sayama, T.; Takara, K. T.

2017-12-01

Many studies in data assimilation in hydrology have focused on the integration of satellite remote sensing and in-situ monitoring data into hydrologic or land surface models. For flood predictions also, recent studies have demonstrated to assimilate remotely sensed inundation information with flood inundation models. In actual flood disaster situations, citizen collected information including local reports by residents and rescue teams and more recently tweets via social media also contain valuable information. The main interest of this study is how to effectively use such citizen collected information for real-time flood hazard mapping. Here we propose a new data assimilation technique based on pre-conducted ensemble inundation simulations and update inundation depth distributions sequentially when local data becomes available. The propose method is composed by the following two-steps. The first step is based on weighting average of preliminary ensemble simulations, whose weights are updated by Bayesian approach. The second step is based on an optimal interpolation, where the covariance matrix is calculated from the ensemble simulations. The proposed method was applied to case studies including an actual flood event occurred. It considers two situations with more idealized one by assuming continuous flood inundation depth information is available at multiple locations. The other one, which is more realistic case during such a severe flood disaster, assumes uncertain and non-continuous information is available to be assimilated. The results show that, in the first idealized situation, the large scale inundation during the flooding was estimated reasonably with RMSE < 0.4 m in average. For the second more realistic situation, the error becomes larger (RMSE 0.5 m) and the impact of the optimal interpolation becomes comparatively less effective. Nevertheless, the applications of the proposed data assimilation method demonstrated a high potential of this method for assimilating citizen collected information for real-time flood hazard mapping in the future.

National Operational Hydrologic Remote Sensing Center - The ultimate source

Science.gov Websites

Analysis Satellite Obs Forecasts Data Archive SHEF Products Observations near City, ST Go Science Database Airborne Snow Surveys Satellite Snow Cover Mapping Snow Modeling and Data Assimilation Analyses polar-orbiting and geostationary satellite imagery. Maps are provided for the U.S. and the northern

DARLA: Data Assimilation and Remote Sensing for Littoral Applications

DTIC Science & Technology

2017-03-01

in the surf zone. The foam produced in an actively breaking crest, or wave roller, has a distinct signature in IR imagery. A retrieval algorithm is...the surface. The velocity profiles are obtained from a pulse-coherent acoustic Doppler sonar on a wave-following platform, termed a Surface Wave

Role of subsurface physics in the assimilation of surface soil moisture observations

USDA-ARS?s Scientific Manuscript database

Soil moisture controls the exchange of water and energy between the land surface and the atmosphere and exhibits memory that may be useful for climate prediction at monthly time scales. Though spatially distributed observations of soil moisture are increasingly becoming available from remotely sense...

Assimilation of MODIS Ice Surface Temperature and Albedo into the Snow and Ice Model CROCUS Over the Greenland Ice Sheet Along the K-transect Stations

NASA Astrophysics Data System (ADS)

Navari, M.; Margulis, S. A.; Bateni, S. M.; Alexander, P. M.; Tedesco, M.

2016-12-01

Estimating the Greenland Ice Sheet (GrIS) surface mass balance (SMB) is an important component of current and future projections of sea level rise. In situ measurement provides direct estimates of the SMB, but are inherently limited by their spatial extent and representativeness. Given this limitation, physically based regional climate models (RCMs) are critical for understanding GrIS physical processes and estimating of the GrIS SMB. However, the uncertainty in estimates of SMB from RCMs is still high. Surface remote sensing (RS) has been used as a complimentary tool to characterize various aspects related to the SMB. The difficulty of using these data streams is that the links between them and the SMB terms are most often indirect and implicit. Given the lack of in situ information, imperfect models, and under-utilized RS data it is critical to merge the available data in a systematic way to better characterize the spatial and temporal variation of the GrIS SMB. This work proposes a data assimilation (DA) framework that yields temporally-continuous and physically consistent SMB estimates that benefit from state-of-the-art models and relevant remote sensing data streams. Ice surface temperature (IST) is the most important factor that regulates partitioning of the net radiation into the subsurface snow/ice, sensible and latent heat fluxes and plays a key role in runoff generation. Therefore it can be expected that a better estimate of surface temperature from a data assimilation system would contribute to a better estimate of surface mass fluxes. Albedo plays an important role in the surface energy balance of the GrIS. However, even advanced albedo modules are not adequate to simulate albedo over the GrIS. Therefore, merging remotely sensed albedo product into a physically based model has a potential to improve the estimates of the GrIS SMB. In this work a MODIS-derived IST and a 16-day albedo product are independently assimilated into the snow and ice model CROCUS. Comparison of our results against the in situ SMB measurements over the K-transect stations shows that assimilation of IST does not considerably improve the GrIS SMB terms. The main reason is hypothesized to be due to a cold bias in the IST product. On the other hand, assimilation of 16-day albedo product reduces the RMSE of the posterior estimates of the SMB by 63%.

Environmental Conditions in the Norwegian-Iceland Seas, May 1987.

DTIC Science & Technology

1987-06-01

Ocanography Division auI W May Joseph W. McCaffrey Ocean Science Directorate Ocean Sensing and Prediction Division June 1987 88330 8 I - I PRELIMINARY REPORT...Leonard Walstad) using an open ocean model; and (4) ship observations aboard the West German ship PLANET . This report concentrates on the first two...validation and delivery for operational use of oceanic and acoustic numerical forecast systems, and the assimilation of in-situ and remotely sensed data

Usefulness of Wave Data Assimilation to the WAVE WATCH III Modeling System

NASA Astrophysics Data System (ADS)

Choi, J. K.; Dykes, J. D.; Yaremchuk, M.; Wittmann, P.

2017-12-01

In-situ and remote-sensed wave data are more abundant currently than in years past, with excellent accuracy at global scales. Forecast skill of the WAVE WATCH III model is improved by assimilation of these measurements and they are also useful for model validation and calibration. It has been known that the impact of assimilation in wind-sea conditions is not large, but spectra that result in large swell with long term propagation are identified and assimilated, the improved accuracy of the initial conditions improve the long-term forecasts. The Navy's assimilation method started with the simple Optimal Interpolation (OI) method. Operationally, Fleet Numerical Meteorology and Oceanography Center uses the sequential 2DVar scheme, but a new approach has been tested based on an adjoint-free method to variational assimilation in WAVE WATCH III. We will present the status of wave data assimilation into the WAVE WATCH III numerical model and upcoming development of this new adjoint-free variational approach.

Towards SMOS: The 2006 National Airborne Field Experiment Plan

NASA Astrophysics Data System (ADS)

Walker, J. P.; Merlin, O.; Panciera, R.; Kalma, J. D.

2006-05-01

The 2006 National Airborne Field Experiment (NAFE) is the second in a series of two intensive experiments to be conducted in different parts of Australia. The NAFE'05 experiment was undertaken in the Goulburn River catchment during November 2005, with the objective to provide high resolution data for process level understanding of soil moisture retrieval, scaling and data assimilation. The NAFE'06 experiment will be undertaken in the Murrumbidgee catchment during November 2006, with the objective to provide data for SMOS (Soil Moisture and Ocean Salinity) level soil moisture retrieval, downscaling and data assimilation. To meet this objective, PLMR (Polarimetric L-band Multibeam Radiometer) and supporting instruments (TIR and NDVI) will be flown at an altitude of 10,000 ft AGL to provide 1km resolution passive microwave data (and 20m TIR) across a 50km x 50km area every 2-3 days. This will both simulate a SMOS pixel and provide the 1km soil moisture data required for downscale verification, allowing downscaling and near-surface soil moisture assimilation techniques to be tested with remote sensing data which is consistent with that from current (MODIS) and planned (SMOS) satellite sensors.. Additionally, two transects will be flown across the area to provide both 1km multi-angular passive microwave data for SMOS algorithm development, and on the same day, 50m resolution passive microwave data for algorithm verification. The study area contains a total of 13 soil moisture profile and rainfall monitoring sites for assimilation verification, and the transect fight lines are planned to go through 5 of these. Ground monitoring of surface soil moisture and vegetation for algorithm verification will be targeted at these 5 focus farms, with soil moisture measurements made at 250m spacing for 1km resolution flights and 50m spacing for 50m resolution flights. While this experiment has a particular emphasis on the remote sensing of soil moisture, it is open for collaboration from interested scientists from all disciplines of environmental remote sensing and its application. See www.nafe.unimelb.edu.au for more detailed information on these experiments.

Data Assimilation using observed streamflow and remotely-sensed soil moisture for improving sub-seasonal-to-seasonal forecasting

NASA Astrophysics Data System (ADS)

Arumugam, S.; Mazrooei, A.; Lakshmi, V.; Wood, A.

2017-12-01

Subseasonal-to-seasonal (S2S) forecasts of soil moisture and streamflow provides critical information for water and agricultural systems to support short-term planning and mangement. This study evaluates the role of observed streamflow and remotely-sensed soil moisture from SMAP (Soil Moisture Active Passive) mission in improving S2S streamflow and soil moisture forecasting using data assimilation (DA). We first show the ability to forecast soil moisture at monthly-to-seaasonal time scale by forcing climate forecasts with NASA's Land Information System and then compares the developed soil moisture forecast with the SMAP data over the Southeast US. Our analyses show significant skill in forecasting real-time soil moisture over 1-3 months using climate information. We also show that the developed soil moisture forecasts capture the observed severe drought conditions (2007-2008) over the Southeast US. Following that, we consider both SMAP data and observed streamflow for improving S2S streamflow and soil moisture forecasts for a pilot study area, Tar River basin, in NC. Towards this, we consider variational assimilation (VAR) of gauge-measured daily streamflow data in improving initial hydrologic conditions of Variable Infiltration Capacity (VIC) model. The utility of data assimilation is then assessed in improving S2S forecasts of streamflow and soil moisture through a retrospective analyses. Furthermore, the optimal frequency of data assimilation and optimal analysis window (number of past observations to use) are also assessed in order to achieve the maximum improvement in S2S forecasts of streamflow and soil moisture. Potential utility of updating initial conditions using DA and providing skillful forcings are also discussed.

Assimilation of Remotely Sensed Leaf Area Index into the Community Land Model with Explicit Carbon and Nitrogen Components using Data Assimilation Research Testbed

NASA Astrophysics Data System (ADS)

Ling, X.; Fu, C.; Yang, Z. L.; Guo, W.

2017-12-01

Information of the spatial and temporal patterns of leaf area index (LAI) is crucial to understand the exchanges of momentum, carbon, energy, and water between the terrestrial ecosystem and the atmosphere, while both in-situ observation and model simulation usually show distinct deficiency in terms of LAI coverage and value. Land data assimilation, combined with observation and simulation together, is a promising way to provide variable estimation. The Data Assimilation Research Testbed (DART) developed and maintained by the National Centre for Atmospheric Research (NCAR) provides a powerful tool to facilitate the combination of assimilation algorithms, models, and real (as well as synthetic) observations to better understanding of all three. Here we systematically investigated the effects of data assimilation on improving LAI simulation based on NCAR Community Land Model with the prognostic carbon-nitrogen option (CLM4CN) linked with DART using the deterministic Ensemble Adjustment Kalman Filter (EAKF). Random 40-member atmospheric forcing was used to drive the CLM4CN with or without LAI assimilation. The Global Land Surface Satellite LAI data (GLASS LAI) LAI is assimilated into the CLM4CN at a frequency of 8 days, and LAI (and leaf carbon / nitrogen) are adjusted at each time step. The results show that assimilating remotely sensed LAI into the CLM4CN is an effective method for improving model performance. In detail, the CLM4-CN simulated LAI systematically overestimates global LAI, especially in low latitude with the largest bias of 5 m2/m2. While if updating both LAI and leaf carbon and leaf nitrogen simultaneously during assimilation, the analyzed LAI can be corrected, especially in low latitude regions with the bias controlled around ±1 m2/m2. Analyzed LAI could also represent the seasonal variation except for the Southern Temperate (23°S-90°S). The obviously improved regions located in the center of Africa, Amazon, the South of Eurasia, the northeast of China, and the west of Europe, where were mainly covered by evergreen/deciduous forests and mixed forests. In addition, the best method for LAI assimilation should include the EAKF method, the accepted percentage of all observation, as well as the carbon-nitrogen control.

Improved Mapping of Carbon, Water and Energy Land-Surface Fluxes Using Remotely Sensed Indicators of Canopy Light Use Efficiency

NASA Astrophysics Data System (ADS)

Schull, M. A.; Anderson, M. C.; Kustas, W.; Cammalleri, C.; Houborg, R.

2012-12-01

A light-use-efficiency (LUE) based model of canopy resistance has been embedded into a thermal-based Two-Source Energy Balance (TSEB) model to facilitate coupled simulations of transpiration and carbon assimilation. The model assumes that deviations of the observed canopy LUE from a nominal stand-level value (LUEn - typically indexed by vegetation class) are due to varying conditions of light, humidity, CO2 concentration and leaf temperature. The deviations are accommodated by adjusting an effective LUE that responds to the varying conditions. The challenge to monitoring fluxes on a larger scale is to capture the physiological responses due to changing conditions. This challenge can be met using remotely sensed leaf chlorophyll (Cab). Since Cab is a vital pigment for absorbing light for use in photosynthesis, it has been recognized as a key parameter for quantifying photosynthetic functioning that are sensitive to these conditions. Recent studies have shown that it is sensitive to changes in LUE, which defines how efficiently a plant can assimilate carbon dioxide (CO2) given the absorbed Photosynthetically Active Radiation (PAR) and is therefore useful for monitoring carbon fluxes. We investigate the feasibility of leaf chlorophyll to capture these variations in LUEn using remotely sensed data. To retrieve Cab from remotely sensed data we use REGFLEC, a physically based tool that translates at-sensor radiances in the green, red and NIR spectral regions from multiple satellite sensors into realistic maps of LAI and Cab. Initial results show that Cab is exponentially correlated to light use efficiency. Incorporating nominal light use efficiency estimated from Cab is shown to improve fluxes of carbon, water and energy most notably in times of stressed vegetation. The result illustrates that Cab is sensitive to changes in plant physiology and can capture plant stress needed for improved estimation of fluxes. The observed relationship and initial results demonstrate the need for integrating remotely sensed Cab to facilitate improved mapping of coupled carbon, water, and energy fluxes across vegetated landscapes.

A review of the remote sensing of lower-tropospheric thermodynamic profiles and its indispensable role for the understanding and the simulation of water and energy cycles

DOE PAGES

Wulfmeyer, Volker; Hardesty, Mike; Turner, David D.; ...

2015-07-08

A review of remote sensing technology for lower-tropospheric thermodynamic (TD) profiling is presented with focus on high accuracy and high temporal-vertical resolution. The contributions of these instruments to the understanding of the Earth system are assessed with respect to radiative transfer, land-surface-atmosphere feedback, convection initiation, and data assimilation. We demonstrate that for progress in weather and climate research, TD profilers are essential. These observational systems must resolve gradients of humidity and temperature in the stable or unstable atmospheric surface layer close to the ground, in the mixed layer, in the interfacial layer – usually characterized by an inversion – andmore » the lower troposphere. A thorough analysis of the current observing systems is performed revealing significant gaps that must be addressed to fulfill existing needs. We analyze whether current and future passive and active remote sensing systems can close these gaps. A methodological analysis and demonstration of measurement capabilities with respect to bias and precision is executed both for passive and active remote sensing including passive infrared and microwave spectroscopy, the global positioning system as well as water-vapor and temperature Raman lidar and water-vapor differential absorption lidar. Whereas passive remote sensing systems are already mature with respect to operational applications, active remote sensing systems require further engineering to become operational in networks. However, active remote sensing systems provide a smaller bias as well as higher temporal and vertical resolutions. For a suitable mesoscale network design, TD profiler system developments should be intensified and dedicated observing system simulation experiments should be performed.« less

A review of the remote sensing of lower-tropospheric thermodynamic profiles and its indispensable role for the understanding and the simulation of water and energy cycles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wulfmeyer, Volker; Hardesty, Mike; Turner, David D.

A review of remote sensing technology for lower-tropospheric thermodynamic (TD) profiling is presented with focus on high accuracy and high temporal-vertical resolution. The contributions of these instruments to the understanding of the Earth system are assessed with respect to radiative transfer, land-surface-atmosphere feedback, convection initiation, and data assimilation. We demonstrate that for progress in weather and climate research, TD profilers are essential. These observational systems must resolve gradients of humidity and temperature in the stable or unstable atmospheric surface layer close to the ground, in the mixed layer, in the interfacial layer – usually characterized by an inversion – andmore » the lower troposphere. A thorough analysis of the current observing systems is performed revealing significant gaps that must be addressed to fulfill existing needs. We analyze whether current and future passive and active remote sensing systems can close these gaps. A methodological analysis and demonstration of measurement capabilities with respect to bias and precision is executed both for passive and active remote sensing including passive infrared and microwave spectroscopy, the global positioning system as well as water-vapor and temperature Raman lidar and water-vapor differential absorption lidar. Whereas passive remote sensing systems are already mature with respect to operational applications, active remote sensing systems require further engineering to become operational in networks. However, active remote sensing systems provide a smaller bias as well as higher temporal and vertical resolutions. For a suitable mesoscale network design, TD profiler system developments should be intensified and dedicated observing system simulation experiments should be performed.« less

Can we reliably estimate managed forest carbon dynamics using remotely sensed data?

NASA Astrophysics Data System (ADS)

Smallman, Thomas Luke; Exbrayat, Jean-Francois; Bloom, A. Anthony; Williams, Mathew

2015-04-01

Forests are an important part of the global carbon cycle, serving as both a large store of carbon and currently as a net sink of CO2. Forest biomass varies significantly in time and space, linked to climate, soils, natural disturbance and human impacts. This variation means that the global distribution of forest biomass and their dynamics are poorly quantified. Terrestrial ecosystem models (TEMs) are rarely evaluated for their predictions of forest carbon stocks and dynamics, due to a lack of knowledge on site specific factors such as disturbance dates and / or managed interventions. In this regard, managed forests present a valuable opportunity for model calibration and improvement. Spatially explicit datasets of planting dates, species and yield classification, in combination with remote sensing data and an appropriate data assimilation (DA) framework can reduce prediction uncertainty and error. We use a Baysian approach to calibrate the data assimilation linked ecosystem carbon (DALEC) model using a Metropolis Hastings-Markov Chain Monte Carlo (MH-MCMC) framework. Forest management information is incorporated into the data assimilation framework as part of ecological and dynamic constraints (EDCs). The key advantage here is that DALEC simulates a full carbon balance, not just the living biomass, and that both parameter and prediction uncertainties are estimated as part of the DA analysis. DALEC has been calibrated at two managed forests, in the USA (Pinus taeda; Duke Forest) and UK (Picea sitchensis; Griffin Forest). At each site DALEC is calibrated twice (exp1 & exp2). Both calibrations (exp1 & exp2) assimilated MODIS LAI and HWSD estimates of soil carbon stored in soil organic matter, in addition to common management information and prior knowledge included in parameter priors and the EDCs. Calibration exp1 also utilises multiple site level estimates of carbon storage in multiple pools. By comparing simulations we determine the impact of site-level observations on uncertainty and error on predictions, and which observations are key to constraining ecosystem processes. Preliminary simulations indicate that DALEC calibration exp1 accurately simulated the assimilated observations for forest and soil carbon stock estimates including, critically for forestry, standing wood stocks (R2 = 0.92, bias = -4.46 MgC ha-1, RMSE = 5.80 MgC ha-1). The results from exp1 indicate the model is able to find parameters that are both consistent with EDC and observations. In the absence of site-level stock observations (exp2) DALEC accurately estimates foliage and fine root pools, while the median estimate of above ground litter and wood stocks (R2 = 0.92, bias = -48.30 MgC ha-1, RMSE = 50.30 MgC ha-1) are over- and underestimated respectively, site-level observations are within model uncertainty. These results indicate that we can estimate managed forests dynamics using remotely sensed data, particularly as remotely sensed above ground biomass maps become available to provide constraint to correct biases in woody accumulation.

Evaluation of Assimilated SMOS Soil Moisture Data for US Cropland Soil Moisture Monitoring

NASA Technical Reports Server (NTRS)

Yang, Zhengwei; Sherstha, Ranjay; Crow, Wade; Bolten, John; Mladenova, Iva; Yu, Genong; Di, Liping

2016-01-01

Remotely sensed soil moisture data can provide timely, objective and quantitative crop soil moisture information with broad geospatial coverage and sufficiently high resolution observations collected throughout the growing season. This paper evaluates the feasibility of using the assimilated ESA Soil Moisture Ocean Salinity (SMOS)Mission L-band passive microwave data for operational US cropland soil surface moisture monitoring. The assimilated SMOS soil moisture data are first categorized to match with the United States Department of Agriculture (USDA)National Agricultural Statistics Service (NASS) survey based weekly soil moisture observation data, which are ordinal. The categorized assimilated SMOS soil moisture data are compared with NASSs survey-based weekly soil moisture data for consistency and robustness using visual assessment and rank correlation. Preliminary results indicate that the assimilated SMOS soil moisture data highly co-vary with NASS field observations across a large geographic area. Therefore, SMOS data have great potential for US operational cropland soil moisture monitoring.

Operational evapotranspiration mapping using remote sensing and weather datasets: a new parameterization for the SSEB approach

USGS Publications Warehouse

Senay, Gabriel B.; Bohms, Stefanie; Singh, Ramesh K.; Gowda, Prasanna H.; Velpuri, Naga Manohar; Alemu, Henok; Verdin, James P.

2013-01-01

The increasing availability of multi-scale remotely sensed data and global weather datasets is allowing the estimation of evapotranspiration (ET) at multiple scales. We present a simple but robust method that uses remotely sensed thermal data and model-assimilated weather fields to produce ET for the contiguous United States (CONUS) at monthly and seasonal time scales. The method is based on the Simplified Surface Energy Balance (SSEB) model, which is now parameterized for operational applications, renamed as SSEBop. The innovative aspect of the SSEBop is that it uses predefined boundary conditions that are unique to each pixel for the "hot" and "cold" reference conditions. The SSEBop model was used for computing ET for 12 years (2000-2011) using the MODIS and Global Data Assimilation System (GDAS) data streams. SSEBop ET results compared reasonably well with monthly eddy covariance ET data explaining 64% of the observed variability across diverse ecosystems in the CONUS during 2005. Twelve annual ET anomalies (2000-2011) depicted the spatial extent and severity of the commonly known drought years in the CONUS. More research is required to improve the representation of the predefined boundary conditions in complex terrain at small spatial scales. SSEBop model was found to be a promising approach to conduct water use studies in the CONUS, with a similar opportunity in other parts of the world. The approach can also be applied with other thermal sensors such as Landsat.

The review of dynamic monitoring technology for crop growth

NASA Astrophysics Data System (ADS)

Zhang, Hong-wei; Chen, Huai-liang; Zou, Chun-hui; Yu, Wei-dong

2010-10-01

In this paper, crop growth monitoring methods are described elaborately. The crop growth models, Netherlands-Wageningen model system, the United States-GOSSYM model and CERES models, Australia APSIM model and CCSODS model system in China, are introduced here more focus on the theories of mechanism, applications, etc. The methods and application of remote sensing monitoring methods, which based on leaf area index (LAI) and biomass were proposed by different scholars at home and abroad, are highly stressed in the paper. The monitoring methods of remote sensing coupling with crop growth models are talked out at large, including the method of "forced law" which using remote sensing retrieval state parameters as the crop growth model parameters input, and then to enhance the dynamic simulation accuracy of crop growth model and the method of "assimilation of Law" which by reducing the gap difference between the value of remote sensing retrieval and the simulated values of crop growth model and thus to estimate the initial value or parameter values to increasing the simulation accuracy. At last, the developing trend of monitoring methods are proposed based on the advantages and shortcomings in previous studies, it is assured that the combination of remote sensing with moderate resolution data of FY-3A, MODIS, etc., crop growth model, "3S" system and observation in situ are the main methods in refinement of dynamic monitoring and quantitative assessment techniques for crop growth in future.

HF Surface Wave Radar for Oceanography -- A Review of Activities in Germany

DTIC Science & Technology

2005-04-14

Environmental and Remote Sensing Center (NERSC). The model and data assimilation technique is described by Breivik and Sætra [2]. Figure 10 shows a...forecasts with the measurements taken at that time, the rms error increases to 20 cm/s. Breivik and Sætra, 2001, present scatter plots and correlations

A land data assimilation system for sub-Saharan Africa food and water security applications

PubMed Central

McNally, Amy; Arsenault, Kristi; Kumar, Sujay; Shukla, Shraddhanand; Peterson, Pete; Wang, Shugong; Funk, Chris; Peters-Lidard, Christa D.; Verdin, James P.

2017-01-01

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWS NET’s operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa. PMID:28195575

A land data assimilation system for sub-Saharan Africa food and water security applications

USGS Publications Warehouse

McNally, Amy; Arsenault, Kristi; Kumar, Sujay; Shukla, Shraddhanand; Peterson, Pete; Wang, Shugong; Funk, Chris; Peters-Lidard, Christa; Verdin, James

2017-01-01

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWS NET’s operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa.

A land data assimilation system for sub-Saharan Africa food and water security applications.

PubMed

McNally, Amy; Arsenault, Kristi; Kumar, Sujay; Shukla, Shraddhanand; Peterson, Pete; Wang, Shugong; Funk, Chris; Peters-Lidard, Christa D; Verdin, James P

2017-02-14

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWS NET's operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa.

Assessment of Evapotranspiration and Soil Moisture Content Across Different Scales of Observation

PubMed Central

Verstraeten, Willem W.; Veroustraete, Frank; Feyen, Jan

2008-01-01

The proper assessment of evapotranspiration and soil moisture content are fundamental in food security research, land management, pollution detection, nutrient flows, (wild-) fire detection, (desert) locust, carbon balance as well as hydrological modelling; etc. This paper takes an extensive, though not exhaustive sample of international scientific literature to discuss different approaches to estimate land surface and ecosystem related evapotranspiration and soil moisture content. This review presents: (i)a summary of the generally accepted cohesion theory of plant water uptake and transport including a shortlist of meteorological and plant factors influencing plant transpiration;(ii)a summary on evapotranspiration assessment at different scales of observation (sap-flow, porometer, lysimeter, field and catchment water balance, Bowen ratio, scintillometer, eddy correlation, Penman-Monteith and related approaches);(iii)a summary on data assimilation schemes conceived to estimate evapotranspiration using optical and thermal remote sensing; and(iv)for soil moisture content, a summary on soil moisture retrieval techniques at different spatial and temporal scales is presented. Concluding remarks on the best available approaches to assess evapotranspiration and soil moisture content with and emphasis on remote sensing data assimilation, are provided. PMID:27879697

Assessment of Evapotranspiration and Soil Moisture Content Across Different Scales of Observation.

PubMed

Verstraeten, Willem W; Veroustraete, Frank; Feyen, Jan

2008-01-09

The proper assessment of evapotranspiration and soil moisture content arefundamental in food security research, land management, pollution detection, nutrient flows,(wild-) fire detection, (desert) locust, carbon balance as well as hydrological modelling; etc.This paper takes an extensive, though not exhaustive sample of international scientificliterature to discuss different approaches to estimate land surface and ecosystem relatedevapotranspiration and soil moisture content. This review presents:(i) a summary of the generally accepted cohesion theory of plant water uptake andtransport including a shortlist of meteorological and plant factors influencing planttranspiration;(ii) a summary on evapotranspiration assessment at different scales of observation (sapflow,porometer, lysimeter, field and catchment water balance, Bowen ratio,scintillometer, eddy correlation, Penman-Monteith and related approaches);(iii) a summary on data assimilation schemes conceived to estimate evapotranspirationusing optical and thermal remote sensing; and(iv) for soil moisture content, a summary on soil moisture retrieval techniques atdifferent spatial and temporal scales is presented.Concluding remarks on the best available approaches to assess evapotranspiration and soilmoisture content with and emphasis on remote sensing data assimilation, are provided.

Data Descriptor: A Land Data Assimilation System for Sub-Saharan Africa Food and Water Security Applications

NASA Technical Reports Server (NTRS)

McNally, Amy; Arsenault, Krist; Kumar, Sujay; Shukla, Shraddhanand; Peter, Pete; Wang, Shugong; Funk, Chris; Peters-Lidard, Christa D.; Verdin, James

2017-01-01

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWSNETs operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa.

A land data assimilation system for sub-Saharan Africa food and water security applications

NASA Astrophysics Data System (ADS)

McNally, Amy; Arsenault, Kristi; Kumar, Sujay; Shukla, Shraddhanand; Peterson, Pete; Wang, Shugong; Funk, Chris; Peters-Lidard, Christa D.; Verdin, James P.

2017-02-01

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWS NET's operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa.

A Conceptual Approach to Assimilating Remote Sensing Data to Improve Soil Moisture Profile Estimates in a Surface Flux/Hydrology Model. Part 1; Overview

NASA Technical Reports Server (NTRS)

Crosson, William L.; Laymon, Charles A.; Inguva, Ramarao; Schamschula, Marius; Caulfield, John

1998-01-01

Knowledge of the amount of water in the soil is of great importance to many earth science disciplines. Soil moisture is a key variable in controlling the exchange of water and energy between the land surface and the atmosphere. Thus, soil moisture information is valuable in a wide range of applications including weather and climate, runoff potential and flood control, early warning of droughts, irrigation, crop yield forecasting, soil erosion, reservoir management, geotechnical engineering, and water quality. Despite the importance of soil moisture information, widespread and continuous measurements of soil moisture are not possible today. Although many earth surface conditions can be measured from satellites, we still cannot adequately measure soil moisture from space. Research in soil moisture remote sensing began in the mid 1970s shortly after the surge in satellite development. Recent advances in remote sensing have shown that soil moisture can be measured, at least qualitatively, by several methods. Quantitative measurements of moisture in the soil surface layer have been most successful using both passive and active microwave remote sensing, although complications arise from surface roughness and vegetation type and density. Early attempts to measure soil moisture from space-borne microwave instruments were hindered by what is now considered sub-optimal wavelengths (shorter than 5 cm) and the coarse spatial resolution of the measurements. L-band frequencies between 1 and 3 GHz (10-30 cm) have been deemed optimal for detection of soil moisture in the upper few centimeters of soil. The Electronically Steered Thinned Array Radiometer (ESTAR), an aircraft-based instrument operating a 1,4 GHz, has shown great promise for soil moisture determination. Initiatives are underway to develop a similar instrument for space. Existing space-borne synthetic aperture radars (SARS) operating at C- and L-band have also shown some potential to detect surface wetness. The advantage of radar is its much higher resolution than passive microwave systems, but it is currently hampered by surface roughness effects and the lack of a good algorithm based on a single frequency and single polarization. In addition, its repeat frequency is generally low (about 40 days). In the meantime, two new radiometers offer some hope for remote sensing of soil moisture from space. The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), launched in November 1997, possesses a 10.65 GHz channel and the Advanced Microwave Scanning Radiometer (AMSR) on both the ADEOS-11 and Earth Observing System AM-1 platforms to be launched in 1999 possesses a 6.9 GHz channel. Aside from issues about interference from vegetation, the coarse resolution of these data will provide considerable challenges pertaining to their application. The resolution of TMI is about 45 km and that of AMSR is about 70 km. These resolutions are grossly inconsistent with the scale of soil moisture processes and the spatial variability of factors that control soil moisture. Scale disparities such as these are forcing us to rethink how we assimilate data of various scales in hydrologic models. Of particular interest is how to assimilate soil moisture data by reconciling the scale disparity between what we can expect from present and future remote sensing measurements of soil moisture and modeling soil moisture processes. It is because of this disparity between the resolution of space-based sensors and the scale of data needed for capturing the spatial variability of soil moisture and related properties that remote sensing of soil moisture has not met with more widespread success. Within a single footprint of current sensors at the wavelengths optimal for this application, in most cases there is enormous heterogeneity in soil moisture created by differences in landcover, soils and topography, as well as variability in antecedent precipitation. It is difficult to interpret the meaning of 'mean' soil moisture under such conditions and even more difficult to apply such a value. Because of the non-linear relationships between near-surface soil moisture and other variables of interest, such as surface energy fluxes and runoff, mean soil moisture has little applicability at such large scales. It is for these reasons that the use of remote sensing in conjunction with a hydrologic model appears to be of benefit in capturing the complete spatial and temporal structure of soil moisture. This paper is Part I of a four-part series describing a method for intermittently assimilating remotely-sensed soil moisture information to improve performance of a distributed land surface hydrology model. The method, summarized in section II, involves the following components, each of which is detailed in the indicated section of the paper or subsequent papers in this series: Forward radiative transfer model methods (section II and Part IV); Use of a Kalman filter to assimilate remotely-sensed soil moisture estimates with the model profile (section II and Part IV); Application of a soil hydrology model to capture the continuous evolution of the soil moisture profile within and below the root zone (section III); Statistical aggregation techniques (section IV and Part II); Disaggregation techniques using a neural network approach (section IV and Part III); and Maximum likelihood and Bayesian algorithms for inversely solving for the soil moisture profile in the upper few cm (Part IV).

Remote sensing-based estimation of annual soil respiration at two contrasting forest sites

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Ni; Gu, Lianhong; Black, T. Andrew

Here, soil respiration (R s), an important component of the global carbon cycle, can be estimated using remotely sensed data, but the accuracy of this technique has not been thoroughly investigated. In this study, we proposed a methodology for the remote estimation of annual R s at two contrasting FLUXNET forest sites (a deciduous broadleaf forest and an evergreen needleleaf forest). A version of the Akaike's information criterion was used to select the best model from a range of models for annual R s estimation based on the remotely sensed data products from the Moderate Resolution Imaging Spectroradiometer and root-zonemore » soil moisture product derived from assimilation of the NASA Advanced Microwave Scanning Radiometer soil moisture products and a two-layer Palmer water balance model. We found that the Arrhenius-type function based on nighttime land surface temperature (LST-night) was the best model by comprehensively considering the model explanatory power and model complexity at the Missouri Ozark and BC-Campbell River 1949 Douglas-fir sites.« less

[Simulation of water and carbon fluxes in harvard forest area based on data assimilation method].

PubMed

Zhang, Ting-Long; Sun, Rui; Zhang, Rong-Hua; Zhang, Lei

2013-10-01

Model simulation and in situ observation are the two most important means in studying the water and carbon cycles of terrestrial ecosystems, but have their own advantages and shortcomings. To combine these two means would help to reflect the dynamic changes of ecosystem water and carbon fluxes more accurately. Data assimilation provides an effective way to integrate the model simulation and in situ observation. Based on the observation data from the Harvard Forest Environmental Monitoring Site (EMS), and by using ensemble Kalman Filter algorithm, this paper assimilated the field measured LAI and remote sensing LAI into the Biome-BGC model to simulate the water and carbon fluxes in Harvard forest area. As compared with the original model simulated without data assimilation, the improved Biome-BGC model with the assimilation of the field measured LAI in 1998, 1999, and 2006 increased the coefficient of determination R2 between model simulation and flux observation for the net ecosystem exchange (NEE) and evapotranspiration by 8.4% and 10.6%, decreased the sum of absolute error (SAE) and root mean square error (RMSE) of NEE by 17.7% and 21.2%, and decreased the SAE and RMSE of the evapotranspiration by 26. 8% and 28.3%, respectively. After assimilated the MODIS LAI products of 2000-2004 into the improved Biome-BGC model, the R2 between simulated and observed results of NEE and evapotranspiration was increased by 7.8% and 4.7%, the SAE and RMSE of NEE were decreased by 21.9% and 26.3%, and the SAE and RMSE of evapotranspiration were decreased by 24.5% and 25.5%, respectively. It was suggested that the simulation accuracy of ecosystem water and carbon fluxes could be effectively improved if the field measured LAI or remote sensing LAI was integrated into the model.

Experiments with data assimilation in comprehensive air quality models: Impacts on model predictions and observation requirements (Invited)

NASA Astrophysics Data System (ADS)

Mathur, R.

2009-12-01

Emerging regional scale atmospheric simulation models must address the increasing complexity arising from new model applications that treat multi-pollutant interactions. Sophisticated air quality modeling systems are needed to develop effective abatement strategies that focus on simultaneously controlling multiple criteria pollutants as well as use in providing short term air quality forecasts. In recent years the applications of such models is continuously being extended to address atmospheric pollution phenomenon from local to hemispheric spatial scales over time scales ranging from episodic to annual. The need to represent interactions between physical and chemical atmospheric processes occurring at these disparate spatial and temporal scales requires the use of observation data beyond traditional in-situ networks so that the model simulations can be reasonably constrained. Preliminary applications of assimilation of remote sensing and aloft observations within a comprehensive regional scale atmospheric chemistry-transport modeling system will be presented: (1) A methodology is developed to assimilate MODIS aerosol optical depths in the model to represent the impacts long-range transport associated with the summer 2004 Alaskan fires on surface-level regional fine particulate matter (PM2.5) concentrations across the Eastern U.S. The episodic impact of this pollution transport event on PM2.5 concentrations over the eastern U.S. during mid-July 2004, is quantified through the complementary use of the model with remotely-sensed, aloft, and surface measurements; (2) Simple nudging experiments with limited aloft measurements are performed to identify uncertainties in model representations of physical processes and assess the potential use of such measurements in improving the predictive capability of atmospheric chemistry-transport models. The results from these early applications will be discussed in context of uncertainties in the model and in the remote sensing data and needs for defining a future optimum observing strategy.

Living on the edge: Flood risks to societies Balázs M. Fekete, Shahabeddin Afshari Tork and Charles J. Vörösmarty

NASA Astrophysics Data System (ADS)

Fekete, B. M.; Afshari Tork, S.; Vorosmarty, C. J.

2015-12-01

Characterizing hydrological extreme events and assessing their societal impacts is perpetual challenge for hydrologists. Climate models predict that anticipated temperature rise leads to an intensification of the hydrological cycle and to a corresponding increase in the reoccurrence and the severity of extreme events. The societal impact of the hydrological extremes are interlinked with anthropogenic activities therefore the damages to manmade infrastructures are rarely a good measure of the extreme events' magnitudes. Extreme events are rare by definition therefore detecting change in their distributions requires long-term observational records. Currently, only in-situ monitoring time series has the temporal extent necessary for assessing the reoccurrence probabilities of extreme events, but they frequently lack the spatial coverage. Satellite remote sensing is often advocated to provide the required spatial coverage, but satellites have to compromise between spatial and temporal resolutions. Furthermore, the retrieval algorithms are often as complex as comparable hydrological models with similar degree of uncertainties in their parameterization and the validity of the final data products. In addition, anticipated changes over time in the reoccurrence frequencies of extreme events invalidates the stationarity assumption, which is the basis for using past observations to predict the probabilities future extreme events. Probably the best approach to provide more robust predictions of extreme events is the integration of the available data (in-situ and remote sensing) in a comprehensive data assimilation frameworks built on top of adequate hydrological modeling platforms. Our presentation will provide an overview of the current state of hydrological models to support data assimilations and the viable pathways to integrate in-situ and remote sensing observations for flood predictions. We will demonstrate the use of socio-economic data in combination with hydrological data assimilation to assess the resiliency to extreme flood events.

U.S. Eastern Continental Shelf Carbon Cycling (USECoS): Modeling, Data Assimilation, and Analysis

NASA Technical Reports Server (NTRS)

Mannino, Antonio

2008-01-01

Although the oceans play a major role in the uptake of fossil fuel CO2 from the atmosphere, there is much debate about the contribution from continental shelves, since many key shelf fluxes are not yet well quantified: the exchange of carbon across the land-ocean and shelf-slope interfaces, air-sea exchange of CO2, burial, and biological processes including productivity. Our goal is to quantify these carbon fluxes along the eastern U.S. coast using models quantitatively verified by comparison to observations, and to establish a framework for predicting how these fluxes may be modified as a result of climate and land use change. Our research questions build on those addressed with previous NASA funding for the USECoS (U.S. Eastern Continental Shelf Carbon Cycling) project. We have developed a coupled biogeochemical ocean circulation model configured for this study region and have extensively evaluated this model with both in situ and remotely-sensed data. Results indicate that to further reduce uncertainties in the shelf component of the global carbon cycle, future efforts must be directed towards 1) increasing the resolution of the physical model via nesting and 2) making refinements to the biogeochemical model and quantitatively evaluating these via the assimilation of biogeochemical data (in situ and remotely-sensed). These model improvements are essential for better understanding and reducing estimates of uncertainties in current and future carbon transformations and cycling in continental shelf systems. Our approach and science questions are particularly germane to the carbon cycle science goals of the NASA Earth Science Research Program as well as the U.S. Climate Change Research Program and the North American Carbon Program. Our interdisciplinary research team consists of scientists who have expertise in the physics and biogeochemistry of the U.S. eastern continental shelf, remote-sensing data analysis and data assimilative numerical models.

Singularity Analysis: a powerful image processing tool in remote sensing of the oceans

NASA Astrophysics Data System (ADS)

Turiel, A.; Umbert, M.; Hoareau, N.; Ballabrera-Poy, J.; Portabella, M.

2012-04-01

The study of fully developed turbulence has given rise to the development of new methods to describe real data of scalars submitted to the action of a turbulent flow. The application of this brand of methodologies (known as Microcanonical Multifractal Formalism, MMF) on remote sensing ocean maps open new ways to exploit those data for oceanographic purposes. The main technique in MMF is that of Singularity Analysis (SA). By means of SA a singularity exponents is assigned to each point of a given image. The singularity exponent of a given point is a dimensionless measure of the regularity or irregularity of the scalar at that point. Singularity exponents arrange in singularity lines, which accurately track the flow streamlines from any scalar, as we have verified with remote sensing and simulated data. Applications of SA include quality assessment of different products, the estimation of surface velocities, the development of fusion techniques for different types of scalars, comparison with measures of ocean mixing, and improvement in assimilation schemes.

Intelligent Systems: Terrestrial Observation and Prediction Using Remote Sensing Data

NASA Technical Reports Server (NTRS)

Coughlan, Joseph C.

2005-01-01

NASA has made science and technology investments to better utilize its large space-borne remote sensing data holdings of the Earth. With the launch of Terra, NASA created a data-rich environment where the challenge is to fully utilize the data collected from EOS however, despite unprecedented amounts of observed data, there is a need for increasing the frequency, resolution, and diversity of observations. Current terrestrial models that use remote sensing data were constructed in a relatively data and compute limited era and do not take full advantage of on-line learning methods and assimilation techniques that can exploit these data. NASA has invested in visualization, data mining and knowledge discovery methods which have facilitated data exploitation, but these methods are insufficient for improving Earth science models that have extensive background knowledge nor do these methods refine understanding of complex processes. Investing in interdisciplinary teams that include computational scientists can lead to new models and systems for online operation and analysis of data that can autonomously improve in prediction skill over time.

Determination of Winter Wheat Phenology in Bavaria- A Contribution to Regional Crop Health Monitoring from Space

NASA Astrophysics Data System (ADS)

Bruggemann, Lena; Bach, Heike; Ruf, Tobias; Appel, Florian; Migdall, Silke; Hank, Tobias; Mauser, Wolfram; Eiblmeier, Peter

2016-08-01

The central topic of this study is the monitoring of winter wheat phenology and the detection of anthesis (flowering) using remotely sensed data as well as crop growth modeling. It is not possible to directly observe the flowering of wheat with optical satellite sensors. Thus, an approach that combines crop growth modeling with remote sensing data covering optical and microwave spectral ranges was developed. This was done in three steps: The hydro-agroecological land surface model PROMET was first run in a stand-alone version for selected sites distributed throughout Bavaria using only static input parameters (e.g. soil map) and current meteorological data as driving factors. Thus, multitemporal information from optical remote sensing data was assimilated into the model runs in a second step to improve the accuracy of the results. Finally, the use of radar data for anthesis detection in winter wheat was tested using Sentinel-1 data of 2015 in dual polarization mode (VV+VH).

Ensemble Data Assimilation for Streamflow Forecasting: Experiments with Ensemble Kalman Filter and Particle Filter

NASA Astrophysics Data System (ADS)

Hirpa, F. A.; Gebremichael, M.; Hopson, T. M.; Wojick, R.

2011-12-01

We present results of data assimilation of ground discharge observation and remotely sensed soil moisture observations into Sacramento Soil Moisture Accounting (SACSMA) model in a small watershed (1593 km2) in Minnesota, the Unites States. Specifically, we perform assimilation experiments with Ensemble Kalman Filter (EnKF) and Particle Filter (PF) in order to improve streamflow forecast accuracy at six hourly time step. The EnKF updates the soil moisture states in the SACSMA from the relative errors of the model and observations, while the PF adjust the weights of the state ensemble members based on the likelihood of the forecast. Results of the improvements of each filter over the reference model (without data assimilation) will be presented. Finally, the EnKF and PF are coupled together to further improve the streamflow forecast accuracy.

Triple collocation-based estimation of spatially correlated observation error covariance in remote sensing soil moisture data assimilation

NASA Astrophysics Data System (ADS)

Wu, Kai; Shu, Hong; Nie, Lei; Jiao, Zhenhang

2018-01-01

Spatially correlated errors are typically ignored in data assimilation, thus degenerating the observation error covariance R to a diagonal matrix. We argue that a nondiagonal R carries more observation information making assimilation results more accurate. A method, denoted TC_Cov, was proposed for soil moisture data assimilation to estimate spatially correlated observation error covariance based on triple collocation (TC). Assimilation experiments were carried out to test the performance of TC_Cov. AMSR-E soil moisture was assimilated with a diagonal R matrix computed using the TC and assimilated using a nondiagonal R matrix, as estimated by proposed TC_Cov. The ensemble Kalman filter was considered as the assimilation method. Our assimilation results were validated against climate change initiative data and ground-based soil moisture measurements using the Pearson correlation coefficient and unbiased root mean square difference metrics. These experiments confirmed that deterioration of diagonal R assimilation results occurred when model simulation is more accurate than observation data. Furthermore, nondiagonal R achieved higher correlation coefficient and lower ubRMSD values over diagonal R in experiments and demonstrated the effectiveness of TC_Cov to estimate richly structuralized R in data assimilation. In sum, compared with diagonal R, nondiagonal R may relieve the detrimental effects of assimilation when simulated model results outperform observation data.

DARLA: Data Assimilation and Remote Sensing for Littoral Applications

DTIC Science & Technology

2013-09-30

of a hydraulic jump around Jetty A. We also obverse the signature of a soliton wave east of Jetty A ( ). West of the mouth, the orbital velocity of...surface flows. (B) Plan view of the bathymetry [Jesse McNinch] (color contours, red is 1 m depth, dark blue is 10 m depth) and near surface flows

The potential of remotely sensed soil moisture for operational flood forecasting

NASA Astrophysics Data System (ADS)

Wanders, N.; Karssenberg, D.; de Roo, A.; de Jong, S.; Bierkens, M. F.

2013-12-01

Nowadays, remotely sensed soil moisture is readily available from multiple space born sensors. The high temporal resolution and global coverage make these products very suitable for large-scale land-surface applications. The potential to use these products in operational flood forecasting has thus far not been extensively studied. In this study, we evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the timing and height of the flood peak and low flows. EFAS is used for operational flood forecasting in Europe and uses a distributed hydrological model for flood predictions for lead times up to 10 days. Satellite-derived soil moisture from ASCAT, AMSR-E and SMOS is assimilated into the EFAS system for the Upper Danube basin and results are compared to assimilation of only discharge observations. Discharge observations are available at the outlet and at six additional locations throughout the catchment. To assimilate soil moisture data into EFAS, an Ensemble Kalman Filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, derived from a detailed model-satellite soil moisture comparison study, is included to ensure optimal performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation dataset. Our results show that the accuracy of flood forecasts is increased when more discharge observations are used in that the Mean Absolute Error (MAE) of the ensemble mean is reduced by 65%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of base flows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the Continuous Ranked Probability Score (CRPS) shows a performance increase of 10-15% on average, compared to assimilation of discharge only. The rank histograms show that the forecast is not biased. The timing errors in the flood predictions are decreased when soil moisture data is used and imminent floods can be forecasted with skill one day earlier. In conclusion, our study shows that assimilation of satellite soil moisture increases the performance of flood forecasting systems for large catchments, like the Upper Danube. The additional gain is highest when discharge observations from both upstream and downstream areas are used in combination with the soil moisture data. These results show the potential of future soil moisture missions with a higher spatial resolution like SMAP to improve near-real time flood forecasting in large catchments.

4DVAR data Assimilation with the Regional Ocean Modeling System (ROMS): Impact on the Water Mass Distributions in the Yellow Sea

NASA Astrophysics Data System (ADS)

Lee, Joon-Ho; Kim, Taekyun; Pang, Ig-Chan; Moon, Jae-Hong

2018-04-01

In this study, we evaluate the performance of the recently developed incremental strong constraint 4-dimensional variational (4DVAR) data assimilation applied to the Yellow Sea (YS) using the Regional Ocean Modeling System (ROMS). Two assimilation experiments are compared: assimilating remote-sensed sea surface temperature (SST) and both the SST and in-situ profiles measured by shipboard CTD casts into a regional ocean modeling from January to December of 2011. By comparing the two assimilation experiments against a free-run without data assimilation, we investigate how the assimilation affects the hydrographic structures in the YS. Results indicate that the SST assimilation notably improves the model behavior at the surface when compared to the nonassimilative free-run. The SST assimilation also has an impact on the subsurface water structure in the eastern YS; however, the improvement is seasonally dependent, that is, the correction becomes more effective in winter than in summer. This is due to a strong stratification in summer that prevents the assimilation of SST from affecting the subsurface temperature. A significant improvement to the subsurface temperature is made when the in-situ profiles of temperature and salinity are assimilated, forming a tongue-shaped YS bottom cold water from the YS toward the southwestern seas of Jeju Island.

Leveraging this Golden Age of Remote Sensing and Modeling of Terrestrial Hydrology to Understand Water Cycling in the Water Availability Grand Challenge for North America

NASA Astrophysics Data System (ADS)

Painter, T. H.; Famiglietti, J. S.; Stephens, G. L.

2016-12-01

We live in a time of increasing strains on our global fresh water availability due to increasing population, warming climate, changes in precipitation, and extensive depletion of groundwater supplies. At the same time, we have seen enormous growth in capabilities to remotely sense the regional to global water cycle and model complex systems with physically based frameworks. The GEWEX Water Availability Grand Challenge for North America is poised to leverage this convergence of remote sensing and modeling capabilities to answer fundamental questions on the water cycle. In particular, we envision an experiment that targets the complex and resource-critical Western US from California to just into the Great Plains, constraining physically-based hydrologic modeling with the US and international remote sensing capabilities. In particular, the last decade has seen the implementation or soon-to-be launch of water cycle missions such as GRACE and GRACE-FO for groundwater, SMAP for soil moisture, GPM for precipitation, SWOT for terrestrial surface water, and the Airborne Snow Observatory for snowpack. With the advent of convection-resolving mesoscale climate and water cycle modeling (e.g. WRF, WRF-Hydro) and mesoscale models capable of quantitative assimilation of remotely sensed data (e.g. the JPL Western States Water Mission), we can now begin to test hypotheses on the nature and changes in the water cycle of the Western US from a physical standpoint. In turn, by fusing water cycle science, water management, and ecosystem management while addressing these hypotheses, this golden age of remote sensing and modeling can bring all fields into a markedly less uncertain state of present knowledge and decadal scale forecasts.

Short-Term Retrospective Land Data Assimilation Schemes

NASA Technical Reports Server (NTRS)

Houser, P. R.; Cosgrove, B. A.; Entin, J. K.; Lettenmaier, D.; ODonnell, G.; Mitchell, K.; Marshall, C.; Lohmann, D.; Schaake, J. C.; Duan, Q.;

A Conceptual Approach to Assimilating Remote Sensing Data to Improve Soil Moisture Profile Estimates in a Surface Flux/Hydrology Model. 2; Aggregation

NASA Technical Reports Server (NTRS)

Schamschula, Marius; Crosson, William L.; Inguva, Ramarao; Yates, Thomas; Laymen, Charles A.; Caulfield, John

1998-01-01

This is a follow up on the preceding presentation by Crosson. The grid size for remote microwave measurements is much coarser than the hydrological model computational grids. To validate the hydrological models with measurements we propose mechanisms to aggregate the hydrological model outputs for soil moisture to allow comparison with measurements. Weighted neighborhood averaging methods are proposed to facilitate the comparison. We will also discuss such complications as misalignment, rotation and other distortions introduced by a generalized sensor image.

Estimation de l'equivalent en eau de la neige en milieu subarctique du Quebec par teledetection micro-ondes passives

NASA Astrophysics Data System (ADS)

Vachon, Francois

The snow cover (extent, depth and water equivalent) is an important factor in assessing the water balance of a territory. In a context of deregulation of electricity, better knowledge of the quantity of water resulting from snowmelt that will be available for hydroelectric power generation has become a major challenge for the managers of Hydro-Quebec's generating plant. In fact, the snow on the ground represents nearly one third of Hydro-Quebec's annual energy reserve and the proportion is even higher for northern watersheds. Snowcover knowledge would therefore help optimize the management of energy stocks. The issue is especially important when one considers that better management of water resources can lead to substantial economic benefits. The Research Institute of Hydro-Quebec (IREQ), our research partner, is currently attempting to optimize the streamfiow forecasts made by its hydrological models by improving the quality of the inputs. These include a parameter known as the snow water equivalent (SWE) which characterizes the properties of the snow cover. At the present time, SWE data is obtained from in situ measurements, which are both sporadic and scattered, and does not allow the temporal and spatial variability of SWE to be characterized adequately for the needs of hydrological models. This research project proposes to provide the Quebec utility's hydrological models with distributed SWE information about its northern watersheds. The targeted accuracy is 15% for the proposed period of analysis covering the winter months of January, February and March of 2001 to 2006. The methodology is based on the HUT snow emission model and uses the passive microwave remote sensing data acquired by the SSM/I sensor. Monitoring of the temporal and spatial variations in SWE is done by inversion of the model and benefits from the assimilation of in situ data to characterize the state of snow cover during the season. Experimental results show that the assimilation technique of in situ data (density and depth) can reproduce the temporal variations in SWE with a RMSE error of 15.9% (R2=0.76). The analysis of land cover within the SSMI pixels can reduce this error to 14.6% ( R2=0.66) for SWE values below 300 mm. Moreover, the results show that the fluctuations of SWE values are driven by changes in snow depths. Indeed, the use of a constant value for the density of snow is feasible and makes it possible to get as good if not better results. These results will allow IREQ to assess the suitability of using snow cover information provided by the remote sensing data in its forecasting models. This improvement in SWE characterization will meet the needs of IREQ for its work on optimization of the quality of hydrological simulations. The originality and relevance of this work are based primarily on the type of method used to quantify SWE and the site where it is applied. The proposed method focuses on the inversion of the HUT model from passive remote sensing data and assimilates in situ data. Moreover, this approach allows high SWE values (> 300 mm) to be quantified, which was impossible with previous methods. These high SWE values are encountered in areas with large amounts of snow such as northern Quebec. Keywords. remote sensing, microwave, snow water equivalent (SWE), model, retrieval, data assimilation, SWE monitoring, spatialization Complete reference. Vachon, F. (2009) Snow water equivalent retrieval in a subartic environment of Quebec using passive microwave remote sensing. Ph.D. Thesis, Sherbrooke University, Sherbrooke, 211 p.

Future directions of meteorology related to air-quality research.

PubMed

Seaman, Nelson L

2003-06-01

Meteorology is one of the major factors contributing to air-pollution episodes. More accurate representation of meteorological fields has been possible in recent years through the use of remote sensing systems, high-speed computers and fine-mesh meteorological models. Over the next 5-20 years, better meteorological inputs for air quality studies will depend on making better use of a wealth of new remotely sensed observations in more advanced data assimilation systems. However, for fine mesh models to be successful, parameterizations used to represent physical processes must be redesigned to be more precise and better adapted for the scales at which they will be applied. Candidates for significant overhaul include schemes to represent turbulence, deep convection, shallow clouds, and land-surface processes. Improvements in the meteorological observing systems, data assimilation and modeling, coupled with advancements in air-chemistry modeling, will soon lead to operational forecasting of air quality in the US. Predictive capabilities can be expected to grow rapidly over the next decade. This will open the way for a number of valuable new services and strategies, including better warnings of unhealthy atmospheric conditions, event-dependent emissions restrictions, and now casting support for homeland security in the event of toxic releases into the atmosphere.

Comparing Global Atmospheric CO2 Flux and Transport Models with Remote Sensing (and Other) Observations

NASA Technical Reports Server (NTRS)

Kawa, S. R.; Collatz, G. J.; Pawson, S.; Wennberg, P. O.; Wofsy, S. C.; Andrews, A. E.

2010-01-01

We report recent progress derived from comparison of global CO2 flux and transport models with new remote sensing and other sources of CO2 data including those from satellite. The overall objective of this activity is to improve the process models that represent our understanding of the workings of the atmospheric carbon cycle. Model estimates of CO2 surface flux and atmospheric transport processes are required for initial constraints on inverse analyses, to connect atmospheric observations to the location of surface sources and sinks, to provide the basic framework for carbon data assimilation, and ultimately for future projections of carbon-climate interactions. Models can also be used to test consistency within and between CO2 data sets under varying geophysical states. Here we focus on simulated CO2 fluxes from terrestrial vegetation and atmospheric transport mutually constrained by analyzed meteorological fields from the Goddard Modeling and Assimilation Office for the period 2000 through 2009. Use of assimilated meteorological data enables direct model comparison to observations across a wide range of scales of variability. The biospheric fluxes are produced by the CASA model at 1x1 degrees on a monthly mean basis, modulated hourly with analyzed temperature and sunlight. Both physiological and biomass burning fluxes are derived using satellite observations of vegetation, burned area (as in GFED-3), and analyzed meteorology. For the purposes of comparison to CO2 data, fossil fuel and ocean fluxes are also included in the transport simulations. In this presentation we evaluate the model's ability to simulate CO2 flux and mixing ratio variability in comparison to remote sensing observations from TCCON, GOSAT, and AIRS as well as relevant in situ observations. Examples of the influence of key process representations are shown from both forward and inverse model comparisons. We find that the model can resolve much of the synoptic, seasonal, and interannual variability in the observations, although reasons for persistent discrepancies in northern hemisphere vegetation uptake are examined. At this time, we do not find any serious shortcomings in the model transport representation, but this is still the subject of close scrutiny. In general, the fidelity of these simulations leads us to anticipate incorporation of real-time, highly resolved remote sensing and other observations into quantitative analyses that will reduce uncertainty in CO2 fluxes and revolutionize our understanding of the key processes controlling atmospheric CO2 and its evolution with time.

Estimating the global terrestrial hydrologic cycle through modeling, remote sensing, and data assimilation

NASA Astrophysics Data System (ADS)

Pan, Ming; Troy, Tara; Sahoo, Alok; Sheffield, Justin; Wood, Eric

2010-05-01

Documentation of the water cycle and its evolution over time is a primary scientific goal of the Global Energy and Water Cycle Experiment (GEWEX) and fundamental to assessing global change impacts. In developed countries, observation systems that include in-situ, remote sensing and modeled data can provide long-term, consistent and generally high quality datasets of water cycle variables. The export of these technologies to less developed regions has been rare, but it is these regions where information on water availability and change is probably most needed in the face of regional environmental change due to climate, land use and water management. In these data sparse regions, in situ data alone are insufficient to develop a comprehensive picture of how the water cycle is changing, and strategies that merge in-situ, model and satellite observations within a framework that results in consistent water cycle records is essential. Such an approach is envisaged by the Global Earth Observing System of Systems (GOESS), but has yet to be applied. The goal of this study is to quantify the variation and changes in the global water cycle over the past 50 years. We evaluate the global water cycle using a variety of independent large-scale datasets of hydrologic variables that are used to bridge the gap between sparse in-situ observations, including remote-sensing based retrievals, observation-forced hydrologic modeling, and weather model reanalyses. A data assimilation framework that blends these disparate sources of information together in a consistent fashion with attention to budget closure is applied to make best estimates of the global water cycle and its variation. The framework consists of a constrained Kalman filter applied to the water budget equation. With imperfect estimates of the water budget components, the equation additionally has an error residual term that is redistributed across the budget components using error statistics, which are estimated from the uncertainties among data products. The constrained Kalman filter treats the budget closure constraint as a perfect observation within the assimilation framework. Precipitation is estimated using gauge observations, reanalysis products, and remote sensing products for below 50°N. Evapotranspiration is estimated in a number of ways: from the VIC land surface hydrologic model forced with a hybrid reanalysis-observation global forcing dataset, from remote sensing retrievals based on a suite of energy balance and process based models, and from an atmospheric water budget approach using reanalysis products for the atmospheric convergence and storage terms and our best estimate for precipitation. Terrestrial water storage changes, including surface and subsurface changes, are estimated using estimates from both VIC and the GRACE remote sensing retrievals. From these components, discharge can then be calculated as a residual of the water budget and compared with gauge observations to evaluate the closure of the water budget. Through the use of these largely independent data products, we estimate both the mean seasonal cycle of the water budget components and their uncertainties for a set of 20 large river basins across the globe. We particularly focus on three regions of interest in global changes studies: the Northern Eurasian region which is experiencing rapid change in terrestrial processes; the Amazon which is a central part of the global water, energy and carbon budgets; and Africa, which is predicted to face some of the most critical challenges for water and food security in the coming decades.

DARLA: Data Assimilation and Remote Sensing for Littoral Applications

DTIC Science & Technology

2011-09-30

measurements from the Surf Zone Optics (SZO) experiment in September, 2011. Since optical reflectance saturates for surf zone bubble depths greater than...Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents...pilot experiment at Duck, NC during the Surf Zone Optics DRI Experiment in September, 2010 and participated in planning of the upcoming RIVET DRI

Reducing the uncertainty in the projection of the terrestrial carbon cycle by fusing models with remote sensing data

NASA Astrophysics Data System (ADS)

Serbin, S.; Shiklomanov, A. N.; Viskari, T.; Desai, A. R.; Townsend, P. A.; Dietze, M.

2015-12-01

Modeling global change requires accurate representation of terrestrial carbon (C), energy and water fluxes. In particular, capturing the properties of vegetation canopies that describe the radiation regime are a key focus for global change research because the properties related to radiation utilization and penetration within plant canopies provide an important constraint on terrestrial ecosystem productivity, as well as the fluxes of water and energy from vegetation to the atmosphere. As such, optical remote sensing observations present an important, and as yet relatively untapped, source of observations that can be used to inform modeling activities. In particular, high-spectral resolution optical data at the leaf and canopy scales offers the potential for an important and direct data constraint on the parameterization and structure of the radiative transfer model (RTM) scheme within ecosystem models across diverse vegetation types, disturbance and management histories. In this presentation we highlight ongoing work to integrate optical remote sensing observations, specifically leaf and imaging spectroscopy (IS) data across a range of forest ecosystems, into complex ecosystem process models within an efficient computational assimilation framework as a means to improve the description of canopy optical properties, vegetation composition, and modeled radiation balance. Our work leverages the Predictive Ecosystem Analyzer (PEcAn; http://www.pecanproject.org/) ecoinformatics toolbox together with a RTM module designed for efficient assimilation of leaf and IS observations to inform vegetation optical properties as well as associated plant traits. Ultimately, an improved understanding of the radiation balance of ecosystems will provide a better constraint on model projections of energy balance, vegetation composition, and carbon pools and fluxes thus allowing for a better diagnosis of the vulnerability of terrestrial ecosystems in response to global change.

Estimation of Multiple Parameters over Vegetated Surfaces by Integrating Optical-Thermal Remote Sensing Observations

NASA Astrophysics Data System (ADS)

Ma, H.

2016-12-01

Land surface parameters from remote sensing observations are critical in monitoring and modeling of global climate change and biogeochemical cycles. Current methods for estimating land surface parameters are generally parameter-specific algorithms and are based on instantaneous physical models, which result in spatial, temporal and physical inconsistencies in current global products. Besides, optical and Thermal Infrared (TIR) remote sensing observations are usually separated to use based on different models , and the Middle InfraRed (MIR) observations have received little attention due to the complexity of the radiometric signal that mixes both reflected and emitted fluxes. In this paper, we proposed a unified algorithm for simultaneously retrieving a total of seven land surface parameters, including Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), land surface albedo, Land Surface Temperature (LST), surface emissivity, downward and upward longwave radiation, by exploiting remote sensing observations from visible to TIR domain based on a common physical Radiative Transfer (RT) model and a data assimilation framework. The coupled PROSPECT-VISIR and 4SAIL RT model were used for canopy reflectance modeling. At first, LAI was estimated using a data assimilation method that combines MODIS daily reflectance observation and a phenology model. The estimated LAI values were then input into the RT model to simulate surface spectral emissivity and surface albedo. Besides, the background albedo and the transmittance of solar radiation, and the canopy albedo were also calculated to produce FAPAR. Once the spectral emissivity of seven MODIS MIR to TIR bands were retrieved, LST can be estimated from the atmospheric corrected surface radiance by exploiting an optimization method. At last, the upward longwave radiation were estimated using the retrieved LST, broadband emissivity (converted from spectral emissivity) and the downward longwave radiation (modeled by MODTRAN). These seven parameters were validated over several representative sites with different biome type, and compared with MODIS and GLASS product. Results showed that this unified inversion algorithm can retrieve temporally complete and physical consistent land surface parameters with high accuracy.

Multi-Scale Three-Dimensional Variational Data Assimilation System for Coastal Ocean Prediction

NASA Technical Reports Server (NTRS)

Li, Zhijin; Chao, Yi; Li, P. Peggy

2012-01-01

A multi-scale three-dimensional variational data assimilation system (MS-3DVAR) has been formulated and the associated software system has been developed for improving high-resolution coastal ocean prediction. This system helps improve coastal ocean prediction skill, and has been used in support of operational coastal ocean forecasting systems and field experiments. The system has been developed to improve the capability of data assimilation for assimilating, simultaneously and effectively, sparse vertical profiles and high-resolution remote sensing surface measurements into coastal ocean models, as well as constraining model biases. In this system, the cost function is decomposed into two separate units for the large- and small-scale components, respectively. As such, data assimilation is implemented sequentially from large to small scales, the background error covariance is constructed to be scale-dependent, and a scale-dependent dynamic balance is incorporated. This scheme then allows effective constraining large scales and model bias through assimilating sparse vertical profiles, and small scales through assimilating high-resolution surface measurements. This MS-3DVAR enhances the capability of the traditional 3DVAR for assimilating highly heterogeneously distributed observations, such as along-track satellite altimetry data, and particularly maximizing the extraction of information from limited numbers of vertical profile observations.

Remote sensing of ocean surface currents: a review of what is being observed and what is being assimilated

NASA Astrophysics Data System (ADS)

Isern-Fontanet, Jordi; Ballabrera-Poy, Joaquim; Turiel, Antonio; García-Ladona, Emilio

2017-10-01

Ocean currents play a key role in Earth's climate - they impact almost any process taking place in the ocean and are of major importance for navigation and human activities at sea. Nevertheless, their observation and forecasting are still difficult. First, no observing system is able to provide direct measurements of global ocean currents on synoptic scales. Consequently, it has been necessary to use sea surface height and sea surface temperature measurements and refer to dynamical frameworks to derive the velocity field. Second, the assimilation of the velocity field into numerical models of ocean circulation is difficult mainly due to lack of data. Recent experiments that assimilate coastal-based radar data have shown that ocean currents will contribute to increasing the forecast skill of surface currents, but require application in multidata assimilation approaches to better identify the thermohaline structure of the ocean. In this paper we review the current knowledge in these fields and provide a global and systematic view of the technologies to retrieve ocean velocities in the upper ocean and the available approaches to assimilate this information into ocean models.

The Global Ocean Data Assimilation Experiment (GODAE)

NASA Astrophysics Data System (ADS)

Le Traon, P.; Smith, N.

The Global Ocean Data Assimilation Experiment (GODAE) will conduct its main demonstration phase from 2003 to 2005. From 2003 to 2005, operational and research institutions from Australia, Japan, United States, United Kingdom, France and Norway will be performing global oceanic data assimilation and ocean forecast in order to provide regular and comprehensive descriptions of ocean fields such as temperature, salinity and currents at high temporal and spatial resolution. A central objective of GODAE is to provide an integrated description that combines remote sensing data, in-situ data and models through data assimilation. Climate and seasonal forecasting, navy applications, marine safety, fisheries, the offshore industry and management of shelf/coastal areas are among the expected beneficiaries of GODAE. The integrated description of the ocean that GODAE will provide will also be highly beneficial to the research community. An overview of GODAE will be given; we will detail the GODAE objectives and strategy and the way it is implemented as an international experiment. Results from first pre-operational or prototype systems will finally be shown.

A study of Bangladesh's sub-surface water storages using satellite products and data assimilation scheme.

PubMed

Khaki, M; Forootan, E; Kuhn, M; Awange, J; Papa, F; Shum, C K

2018-06-01

Climate change can significantly influence terrestrial water changes around the world particularly in places that have been proven to be more vulnerable such as Bangladesh. In the past few decades, climate impacts, together with those of excessive human water use have changed the country's water availability structure. In this study, we use multi-mission remotely sensed measurements along with a hydrological model to separately analyze groundwater and soil moisture variations for the period 2003-2013, and their interactions with rainfall in Bangladesh. To improve the model's estimates of water storages, terrestrial water storage (TWS) data obtained from the Gravity Recovery And Climate Experiment (GRACE) satellite mission are assimilated into the World-Wide Water Resources Assessment (W3RA) model using the ensemble-based sequential technique of the Square Root Analysis (SQRA) filter. We investigate the capability of the data assimilation approach to use a non-regional hydrological model for a regional case study. Based on these estimates, we investigate relationships between the model derived sub-surface water storage changes and remotely sensed precipitations, as well as altimetry-derived river level variations in Bangladesh by applying the empirical mode decomposition (EMD) method. A larger correlation is found between river level heights and rainfalls (78% on average) in comparison to groundwater storage variations and rainfalls (57% on average). The results indicate a significant decline in groundwater storage (∼32% reduction) for Bangladesh between 2003 and 2013, which is equivalent to an average rate of 8.73 ± 2.45mm/year. Copyright © 2018 Elsevier B.V. All rights reserved.

Modeling Gross Primary Production of Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in a Process-Based Model

PubMed Central

Migliavacca, Mirco; Meroni, Michele; Busetto, Lorenzo; Colombo, Roberto; Zenone, Terenzio; Matteucci, Giorgio; Manca, Giovanni; Seufert, Guenther

2009-01-01

In this paper we present results obtained in the framework of a regional-scale analysis of the carbon budget of poplar plantations in Northern Italy. We explored the ability of the process-based model BIOME-BGC to estimate the gross primary production (GPP) using an inverse modeling approach exploiting eddy covariance and satellite data. We firstly present a version of BIOME-BGC coupled with the radiative transfer models PROSPECT and SAILH (named PROSAILH-BGC) with the aims of i) improving the BIOME-BGC description of the radiative transfer regime within the canopy and ii) allowing the assimilation of remotely-sensed vegetation index time series, such as MODIS NDVI, into the model. Secondly, we present a two-step model inversion for optimization of model parameters. In the first step, some key ecophysiological parameters were optimized against data collected by an eddy covariance flux tower. In the second step, important information about phenological dates and about standing biomass were optimized against MODIS NDVI. Results obtained showed that the PROSAILH-BGC allowed simulation of MODIS NDVI with good accuracy and that we described better the canopy radiation regime. The inverse modeling approach was demonstrated to be useful for the optimization of ecophysiological model parameters, phenological dates and parameters related to the standing biomass, allowing good accuracy of daily and annual GPP predictions. In summary, this study showed that assimilation of eddy covariance and remote sensing data in a process model may provide important information for modeling gross primary production at regional scale. PMID:22399948

Modeling gross primary production of agro-forestry ecosystems by assimilation of satellite-derived information in a process-based model.

PubMed

Migliavacca, Mirco; Meroni, Michele; Busetto, Lorenzo; Colombo, Roberto; Zenone, Terenzio; Matteucci, Giorgio; Manca, Giovanni; Seufert, Guenther

2009-01-01

In this paper we present results obtained in the framework of a regional-scale analysis of the carbon budget of poplar plantations in Northern Italy. We explored the ability of the process-based model BIOME-BGC to estimate the gross primary production (GPP) using an inverse modeling approach exploiting eddy covariance and satellite data. We firstly present a version of BIOME-BGC coupled with the radiative transfer models PROSPECT and SAILH (named PROSAILH-BGC) with the aims of i) improving the BIOME-BGC description of the radiative transfer regime within the canopy and ii) allowing the assimilation of remotely-sensed vegetation index time series, such as MODIS NDVI, into the model. Secondly, we present a two-step model inversion for optimization of model parameters. In the first step, some key ecophysiological parameters were optimized against data collected by an eddy covariance flux tower. In the second step, important information about phenological dates and about standing biomass were optimized against MODIS NDVI. Results obtained showed that the PROSAILH-BGC allowed simulation of MODIS NDVI with good accuracy and that we described better the canopy radiation regime. The inverse modeling approach was demonstrated to be useful for the optimization of ecophysiological model parameters, phenological dates and parameters related to the standing biomass, allowing good accuracy of daily and annual GPP predictions. In summary, this study showed that assimilation of eddy covariance and remote sensing data in a process model may provide important information for modeling gross primary production at regional scale.

Rapid prototyping of soil moisture estimates using the NASA Land Information System

NASA Astrophysics Data System (ADS)

Anantharaj, V.; Mostovoy, G.; Li, B.; Peters-Lidard, C.; Houser, P.; Moorhead, R.; Kumar, S.

2007-12-01

The Land Information System (LIS), developed at the NASA Goddard Space Flight Center, is a functional Land Data Assimilation System (LDAS) that incorporates a suite of land models in an interoperable computational framework. LIS has been integrated into a computational Rapid Prototyping Capabilities (RPC) infrastructure. LIS consists of a core, a number of community land models, data servers, and visualization systems - integrated in a high-performance computing environment. The land surface models (LSM) in LIS incorporate surface and atmospheric parameters of temperature, snow/water, vegetation, albedo, soil conditions, topography, and radiation. Many of these parameters are available from in-situ observations, numerical model analysis, and from NASA, NOAA, and other remote sensing satellite platforms at various spatial and temporal resolutions. The computational resources, available to LIS via the RPC infrastructure, support e- Science experiments involving the global modeling of land-atmosphere studies at 1km spatial resolutions as well as regional studies at finer resolutions. The Noah Land Surface Model, available with-in the LIS is being used to rapidly prototype soil moisture estimates in order to evaluate the viability of other science applications for decision making purposes. For example, LIS has been used to further extend the utility of the USDA Soil Climate Analysis Network of in-situ soil moisture observations. In addition, LIS also supports data assimilation capabilities that are used to assimilate remotely sensed soil moisture retrievals from the AMSR-E instrument onboard the Aqua satellite. The rapid prototyping of soil moisture estimates using LIS and their applications will be illustrated during the presentation.

Evaluating the Utility of Remotely-Sensed Soil Moisture Retrievals for Operational Agricultural Drought Monitoring

NASA Technical Reports Server (NTRS)

Bolten, John D.; Crow, Wade T.; Zhan, Xiwu; Jackson, Thomas J.; Reynolds,Curt

2010-01-01

Soil moisture is a fundamental data source used by the United States Department of Agriculture (USDA) International Production Assessment Division (IPAD) to monitor crop growth stage and condition and subsequently, globally forecast agricultural yields. Currently, the USDA IPAD estimates surface and root-zone soil moisture using a two-layer modified Palmer soil moisture model forced by global precipitation and temperature measurements. However, this approach suffers from well-known errors arising from uncertainty in model forcing data and highly simplified model physics. Here we attempt to correct for these errors by designing and applying an Ensemble Kalman filter (EnKF) data assimilation system to integrate surface soil moisture retrievals from the NASA Advanced Microwave Scanning Radiometer (AMSR-E) into the USDA modified Palmer soil moisture model. An assessment of soil moisture analysis products produced from this assimilation has been completed for a five-year (2002 to 2007) period over the North American continent between 23degN - 50degN and 128degW - 65degW. In particular, a data denial experimental approach is utilized to isolate the added utility of integrating remotely-sensed soil moisture by comparing EnKF soil moisture results obtained using (relatively) low-quality precipitation products obtained from real-time satellite imagery to baseline Palmer model runs forced with higher quality rainfall. An analysis of root-zone anomalies for each model simulation suggests that the assimilation of AMSR-E surface soil moisture retrievals can add significant value to USDA root-zone predictions derived from real-time satellite precipitation products.

A Comparison of Methods for a Priori Bias Correction in Soil Moisture Data Assimilation

NASA Technical Reports Server (NTRS)

Kumar, Sujay V.; Reichle, Rolf H.; Harrison, Kenneth W.; Peters-Lidard, Christa D.; Yatheendradas, Soni; Santanello, Joseph A.

2011-01-01

Data assimilation is being increasingly used to merge remotely sensed land surface variables such as soil moisture, snow and skin temperature with estimates from land models. Its success, however, depends on unbiased model predictions and unbiased observations. Here, a suite of continental-scale, synthetic soil moisture assimilation experiments is used to compare two approaches that address typical biases in soil moisture prior to data assimilation: (i) parameter estimation to calibrate the land model to the climatology of the soil moisture observations, and (ii) scaling of the observations to the model s soil moisture climatology. To enable this research, an optimization infrastructure was added to the NASA Land Information System (LIS) that includes gradient-based optimization methods and global, heuristic search algorithms. The land model calibration eliminates the bias but does not necessarily result in more realistic model parameters. Nevertheless, the experiments confirm that model calibration yields assimilation estimates of surface and root zone soil moisture that are as skillful as those obtained through scaling of the observations to the model s climatology. Analysis of innovation diagnostics underlines the importance of addressing bias in soil moisture assimilation and confirms that both approaches adequately address the issue.

Assimilation of Spatially Sparse In Situ Soil Moisture Networks into a Continuous Model Domain

NASA Astrophysics Data System (ADS)

Gruber, A.; Crow, W. T.; Dorigo, W. A.

2018-02-01

Growth in the availability of near-real-time soil moisture observations from ground-based networks has spurred interest in the assimilation of these observations into land surface models via a two-dimensional data assimilation system. However, the design of such systems is currently hampered by our ignorance concerning the spatial structure of error afflicting ground and model-based soil moisture estimates. Here we apply newly developed triple collocation techniques to provide the spatial error information required to fully parameterize a two-dimensional (2-D) data assimilation system designed to assimilate spatially sparse observations acquired from existing ground-based soil moisture networks into a spatially continuous Antecedent Precipitation Index (API) model for operational agricultural drought monitoring. Over the contiguous United States (CONUS), the posterior uncertainty of surface soil moisture estimates associated with this 2-D system is compared to that obtained from the 1-D assimilation of remote sensing retrievals to assess the value of ground-based observations to constrain a surface soil moisture analysis. Results demonstrate that a fourfold increase in existing CONUS ground station density is needed for ground network observations to provide a level of skill comparable to that provided by existing satellite-based surface soil moisture retrievals.

Some new mathematical methods for variational objective analysis

NASA Technical Reports Server (NTRS)

Wahba, Grace; Johnson, Donald R.

1994-01-01

Numerous results were obtained relevant to remote sensing, variational objective analysis, and data assimilation. A list of publications relevant in whole or in part is attached. The principal investigator gave many invited lectures, disseminating the results to the meteorological community as well as the statistical community. A list of invited lectures at meetings is attached, as well as a list of departmental colloquia at various universities and institutes.

The potential impact of scatterometry on oceanography - A wave forecasting case

NASA Technical Reports Server (NTRS)

Cane, M. A.; Cardone, V. J.

1981-01-01

A series of observing system simulation experiments have been performed in order to assess the potential impact of marine surface wind data on numerical weather prediction. In addition to conventional data, the experiments simulated the time-continuous assimilation of remotely sensed marine surface wind or temperature sounding data. The wind data were fabricated directly for model grid points intercepted by a Seasat-1 scatterometer swath and were assimilated into the lowest active level (945 mb) of the model using a localized successive correction method. It is shown that Seasat wind data can greatly improve numerical weather forecasts due to better definition of specific features. The case of the QE II storm is examined.

A Global Data Assimilation System for Atmospheric Aerosol

NASA Technical Reports Server (NTRS)

daSilva, Arlindo

1999-01-01

We will give an overview of an aerosol data assimilation system which combines advances in remote sensing of atmospheric aerosols, aerosol modeling and data assimilation methodology to produce high spatial and temporal resolution 3D aerosol fields. Initially, the Goddard Aerosol Assimilation System (GAAS) will assimilate TOMS, AVHRR and AERONET observations; later we will include MODIS and MISR. This data assimilation capability will allows us to integrate complementing aerosol observations from these platforms, enabling the development of an assimilated aerosol climatology as well as a global aerosol forecasting system in support of field campaigns. Furthermore, this system provides an interactive retrieval framework for each aerosol observing satellites, in particular TOMS and AVHRR. The Goddard Aerosol Assimilation System (GAAS) takes advantage of recent advances in constituent data assimilation at DAO, including flow dependent parameterizations of error covariances and the proper consideration of model bias. For its prognostic transport model, GAAS will utilize the Goddard Ozone, Chemistry, Aerosol, Radiation and Transport (GOCART) model developed at NASA/GSFC Codes 916 and 910.3. GOCART includes the Lin-Rood flux-form, semi-Langrangian transport model with parameterized aerosol chemistry and physical processes for absorbing (dust and black carbon) and non-absorbing aerosols (sulfate and organic carbon). Observations and model fields are combined using a constituent version of DAO's Physical-space Statistical Analysis System (PSAS), including its adaptive quality control system. In this talk we describe the main components of this assimilation system and present preliminary results obtained by assimilating TOMS data.

A Conceptual Approach to Assimilating Remote Sensing Data to Improve Soil Moisture Profile Estimates in a Surface Flux/Hydrology Model. 3; Disaggregation

NASA Technical Reports Server (NTRS)

Caulfield, John; Crosson, William L.; Inguva, Ramarao; Laymon, Charles A.; Schamschula, Marius

1998-01-01

This is a followup on the preceding presentation by Crosson and Schamschula. The grid size for remote microwave measurements is much coarser than the hydrological model computational grids. To validate the hydrological models with measurements we propose mechanisms to disaggregate the microwave measurements to allow comparison with outputs from the hydrological models. Weighted interpolation and Bayesian methods are proposed to facilitate the comparison. While remote measurements occur at a large scale, they reflect underlying small-scale features. We can give continuing estimates of the small scale features by correcting the simple 0th-order, starting with each small-scale model with each large-scale measurement using a straightforward method based on Kalman filtering.

Extraction of Rice Heavy Metal Stress Signal Features Based on Long Time Series Leaf Area Index Data Using Ensemble Empirical Mode Decomposition

PubMed Central

Liu, Xiangnan; Zhang, Biyao; Liu, Ming; Wu, Ling

2017-01-01

The use of remote sensing technology to diagnose heavy metal stress in crops is of great significance for environmental protection and food security. However, in the natural farmland ecosystem, various stressors could have a similar influence on crop growth, therefore making heavy metal stress difficult to identify accurately, so this is still not a well resolved scientific problem and a hot topic in the field of agricultural remote sensing. This study proposes a method that uses Ensemble Empirical Mode Decomposition (EEMD) to obtain the heavy metal stress signal features on a long time scale. The method operates based on the Leaf Area Index (LAI) simulated by the Enhanced World Food Studies (WOFOST) model, assimilated with remotely sensed data. The following results were obtained: (i) the use of EEMD was effective in the extraction of heavy metal stress signals by eliminating the intra-annual and annual components; (ii) LAIdf (The first derivative of the sum of the interannual component and residual) can preferably reflect the stable feature responses to rice heavy metal stress. LAIdf showed stability with an R2 of greater than 0.9 in three growing stages, and the stability is optimal in June. This study combines the spectral characteristics of the stress effect with the time characteristics, and confirms the potential of long-term remotely sensed data for improving the accuracy of crop heavy metal stress identification. PMID:28878147

Crop biomass and evapotranspiration estimation using SPOT and Formosat-2 Data

NASA Astrophysics Data System (ADS)

Veloso, Amanda; Demarez, Valérie; Ceschia, Eric; Claverie, Martin

2013-04-01

The use of crop models allows simulating plant development, growth and yield under different environmental and management conditions. When combined with high spatial and temporal resolution remote sensing data, these models provide new perspectives for crop monitoring at regional scale. We propose here an approach to estimate time courses of dry aboveground biomass, yield and evapotranspiration (ETR) for summer (maize, sunflower) and winter crops (wheat) by assimilating Green Area Index (GAI) data, obtained from satellite observations, into a simple crop model. Only high spatial resolution and gap-free satellite time series can provide enough information for efficient crop monitoring applications. The potential of remote sensing data is often limited by cloud cover and/or gaps in observation. Data from different sensor systems need then to be combined. For this work, we employed a unique set of Formosat-2 and SPOT images (164 images) and in-situ measurements, acquired from 2006 to 2010 in southwest France. Among the several land surface biophysical variables accessible from satellite observations, the GAI is the one that has a key role in soil-plant-atmosphere interactions and in biomass accumulation process. Many methods have been developed to relate GAI to optical remote sensing signal. Here, seasonal dynamics of remotely sensed GAI were estimated by applying a method based on the inversion of a radiative transfer model using artificial neural networks. The modelling approach is based on the Simple Algorithm for Yield and Evapotranspiration estimate (SAFYE) model, which couples the FAO-56 model with an agro-meteorological model, based on Monteith's light-use efficiency theory. The SAFYE model is a daily time step crop model that simulates time series of GAI, dry aboveground biomass, grain yield and ETR. Crop and soil model parameters were determined using both in-situ measurements and values found in the literature. Phenological parameters were calibrated by the assimilation of the remotely sensed GAI time series. The calibration process led to accurate spatial estimates of GAI, ETR as well as of biomass and yield over the study area (24 km x 24 km window). The results highlight the interest of using a combined approach (crop model coupled with high spatial and temporal resolution remote sensing data) for the estimation of agronomical variables. At local scale, the model reproduced correctly the biomass production and ETR for summer crops (with relative RMSE of 29% and 35%, respectively). At regional scale, estimated yield and water requirement for irrigation were compared to regional statistics of yield and irrigation inventories provided by the local water agency. Results showed good agreements for inter-annual dynamics of yield estimates. Differences between water requirement for irrigation and actual supply were lower than 10% and inter-annual variability was well represented as well. The work, initially focused on summer crops, is being adapted to winter crops.

Data Analysis, Modeling, and Ensemble Forecasting to Support NOWCAST and Forecast Activities at the Fallon Naval Station

DTIC Science & Technology

2010-09-30

and climate forecasting and use of satellite data assimilation for model evaluation. He is a task leader on another NSF_EPSCoR project for the...1 DISTRIBUTION STATEMENT A: Approved for public release; distribution is unlimited. Data Analysis, Modeling, and Ensemble Forecasting to...observations including remotely sensed data . OBJECTIVES The main objectives of the study are: 1) to further develop, test, and continue twice daily

Snow multivariable data assimilation for hydrological predictions in mountain areas

NASA Astrophysics Data System (ADS)

Piazzi, Gaia; Campo, Lorenzo; Gabellani, Simone; Rudari, Roberto; Castelli, Fabio; Cremonese, Edoardo; Morra di Cella, Umberto; Stevenin, Hervé; Ratto, Sara Maria

2016-04-01

The seasonal presence of snow on alpine catchments strongly impacts both surface energy balance and water resource. Thus, the knowledge of the snowpack dynamics is of critical importance for several applications, such as water resource management, floods prediction and hydroelectric power production. Several independent data sources provide information about snowpack state: ground-based measurements, satellite data and physical models. Although all these data types are reliable, each of them is affected by specific flaws and errors (respectively dependency on local conditions, sensor biases and limitations, initialization and poor quality forcing data). Moreover, there are physical factors that make an exhaustive reconstruction of snow dynamics complicated: snow intermittence in space and time, stratification and slow phenomena like metamorphism processes, uncertainty in snowfall evaluation, wind transportation, etc. Data Assimilation (DA) techniques provide an objective methodology to combine observational and modeled information to obtain the most likely estimate of snowpack state. Indeed, by combining all the available sources of information, the implementation of DA schemes can quantify and reduce the uncertainties of the estimations. This study presents SMASH (Snow Multidata Assimilation System for Hydrology), a multi-layer snow dynamic model, strengthened by a robust multivariable data assimilation algorithm. The model is physically based on mass and energy balances and can be used to reproduce the main physical processes occurring within the snowpack: accumulation, density dynamics, melting, sublimation, radiative balance, heat and mass exchanges. The model is driven by observed forcing meteorological data (air temperature, wind velocity, relative air humidity, precipitation and incident solar radiation) to provide a complete estimate of snowpack state. The implementation of an Ensemble Kalman Filter (EnKF) scheme enables to assimilate simultaneously ground-based and remotely sensed data of different snow-related variables (snow albedo and surface temperature, Snow Water Equivalent from passive microwave sensors and Snow Cover Area). SMASH performance was evaluated in the period June 2012 - December 2013 at the meteorological station of Torgnon (Tellinod, 2 160 msl), located in Aosta Valley, a mountain region in northwestern Italy. The EnKF algorithm was firstly tested by assimilating several ground-based measurements: snow depth, land surface temperature, snow density and albedo. The assimilation of snow observed data revealed an overall considerable enhancement of model predictions with respect to the open loop experiments. A first attempt to integrate also remote sensed information was performed by assimilating the Land Surface Temperature (LST) from METEOSAT Second Generation (MSG), leading to good results. The analysis allowed identifying the snow depth and the snowpack surface temperature as the most impacting variables in the assimilation process. In order to pinpoint an optimal number of ensemble instances, SMASH performances were also quantitatively evaluated by varying the instances amount. Furthermore, the impact of the data assimilation frequency was analyzed by varying the assimilation time step (3h, 6h, 12h, 24h).

Environmental Public Health Applications Using Remotely Sensed Data.

PubMed

Al-Hamdan, Mohammad Z; Crosson, William L; Economou, Sigrid A; Estes, Maurice G; Estes, Sue M; Hemmings, Sarah N; Kent, Shia T; Puckett, Mark; Quattrochi, Dale A; Rickman, Douglas L; Wade, Gina M; McClure, Leslie A

2014-01-01

We describe a remote sensing and GIS-based study that has three objectives: (1) characterize fine particulate matter (PM 2.5 ), insolation and land surface temperature using NASA satellite observations, EPA ground-level monitor data and North American Land Data Assimilation System (NLDAS) data products on a national scale; (2) link these data with public health data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether these environmental risk factors are related to cognitive decline, stroke and other health outcomes; and (3) disseminate the environmental datasets and public health linkage analyses to end users for decision-making through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This study directly addresses a public health focus of the NASA Applied Sciences Program, utilization of Earth Sciences products, by addressing issues of environmental health to enhance public health decision-making.

Environmental Public Health Applications Using Remotely Sensed Data

PubMed Central

Al-Hamdan, Mohammad Z.; Crosson, William L.; Economou, Sigrid A.; Estes, Maurice G.; Estes, Sue M.; Hemmings, Sarah N.; Kent, Shia T.; Puckett, Mark; Quattrochi, Dale A.; Rickman, Douglas L.; Wade, Gina M.; McClure, Leslie A.

2012-01-01

We describe a remote sensing and GIS-based study that has three objectives: (1) characterize fine particulate matter (PM2.5), insolation and land surface temperature using NASA satellite observations, EPA ground-level monitor data and North American Land Data Assimilation System (NLDAS) data products on a national scale; (2) link these data with public health data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether these environmental risk factors are related to cognitive decline, stroke and other health outcomes; and (3) disseminate the environmental datasets and public health linkage analyses to end users for decision-making through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This study directly addresses a public health focus of the NASA Applied Sciences Program, utilization of Earth Sciences products, by addressing issues of environmental health to enhance public health decision-making. PMID:24910505

LWS Proposal to Provide Scientific Guidance and Modeling Support for the Ionospheric Mapping Mission. Part 1

NASA Technical Reports Server (NTRS)

Richmond, Arthur D.

2005-01-01

A data assimilation system for specifying the thermospheric density has been developed over the last several years. This system ingests GRACE/CHAMP-type in situ as well as SSULI/SSUSI remote sensing observations while making use of a physical model, the Coupled Thermosphere-Ionosphere Model (CTIM) (Fuller-Rowel1 et al., 1996). The Kalman filter was implemented as the backbone to the data assimilation system, which provides a statistically 'best' estimate as well as an estimate of the error in its state. The system was tested using a simulated thermosphere and observations. CHAMP data were then used to provide the system with a real data source. The results of this study are herein.

Modeling of Aerosol Optical Depth Variability during the 1998 Canadian Forest Fire Smoke Event

NASA Astrophysics Data System (ADS)

Aubé, M.; O`Neill, N. T.; Royer, A.; Lavoué, D.

2003-04-01

Monitoring of aerosol optical depth (AOD) is of particular importance due to the significant role of aerosols in the atmospheric radiative budget. Up to now the two standard techniques used for retrieving AOD are; (i) sun photometry which provides measurements of high temporal frequency and sparse spatial frequency, and (ii) satellite based approaches such as based DDV (Dense Dark Vegetation) inversion algorithms which extract AOD over dark targets in remotely sensed imagery. Although the latter techniques allow AOD retrieval over appreciable spatial domains, the irregular spatial pattern of dark targets and the typically low repeat frequencies of imaging satellites exclude the acquisition of AOD databases on a continuous spatio-temporal basis. We attempt to fill gaps in spatio-temporal AOD measurements using a new methodology that links AOD measurements and particulate matter Transport Model using a data assimilation approach. This modelling package (AODSEM for Aerosol Optical Depth Spatio-temporal Evolution Model) uses a size and aerosol type segregated semi-Lagrangian-Eulerian trajectory algorithm driven by analysed meteorological data. Its novelty resides in the fact that the model evolution is tied to both ground based and satellite level AOD measurement and all physical processes have been optimized to track this important but crude parameter. We applied this methodology to a significant smoke event that occurred over Canada in august 1998. The results show the potential of this approach inasmuch as residuals between AODSEM assimilated analysis and measurements are smaller than typical errors associated to remotely sensed AOD (satellite or ground based). The AODSEM assimilation approach also gives better results than classical interpolation techniques. This improvement is especially evident when the available number of AOD measurements is small.

JPL/USC GAIM: Validating COSMIC and Ground-Based GPS Assimilation Results to Estimate Ionospheric Electron Densities

NASA Astrophysics Data System (ADS)

Komjathy, A.; Wilson, B.; Akopian, V.; Pi, X.; Mannucci, A.; Wang, C.

2008-12-01

We seem to be in the midst of a revolution in ionospheric remote sensing driven by the abundance of ground and space-based GPS receivers, new UV remote sensing satellites, and the advent of data assimilation techniques for space weather. In particular, the COSMIC 6-satellite constellation was launched in April 2006. COSMIC now provides unprecedented global coverage of GPS occultations measurements, each of which yields electron density information with unprecedented ~1 km vertical resolution. Calibrated measurements of ionospheric delay (total electron content or TEC) suitable for input into assimilation models is currently made available in near real-time (NRT) from the COSMIC with a latency of 30 to 120 minutes. The University of Southern California (USC) and the Jet Propulsion Laboratory (JPL) have jointly developed a real-time Global Assimilative Ionospheric Model (GAIM) to monitor space weather, study storm effects, and provide ionospheric calibration for DoD customers and NASA flight projects. JPL/USC GAIM is a physics- based 3D data assimilation model that uses both 4DVAR and Kalman filter techniques to solve for the ion and electron density state and key drivers such as equatorial electrodynamics, neutral winds, and production terms. Daily (delayed) GAIM runs can accept as input ground GPS TEC data from 1200+ sites, occultation links from CHAMP, SAC-C, and the COSMIC constellation, UV limb and nadir scans from the TIMED and DMSP satellites, and in situ data from a variety of satellites (DMSP and C/NOFS). Real-Time GAIM (RTGAIM) ingests multiple data sources in real time, updates the 3D electron density grid every 5 minutes, and solves for improved drivers every 1-2 hours. Since our forward physics model and the adjoint model were expressly designed for data assimilation and computational efficiency, all of this can be accomplished on a single dual- processor Unix workstation. Customers are currently evaluating the accuracy of JPL/USC GAIM 'nowcasts' for ray tracing applications and trans-ionospheric path delay calibration. In the presentation, we will discuss the expected impact of NRT COSMIC occultation and NRT ground-based measurements and present validation results for ingest of COSMIC data into GAIM using measurements from World Days. We will quality check our COSMIC-derived products by comparing Abel profiles and JPL- processed results. Furthermore, we will validate GAIM assimilation results using Incoherent Scatter Radar measurements from Arecibo, Jicamarca and Millstone Hill datasets. We will conclude by characterizing the improved electron density states using dual-frequency altimeter-derived Jason vertical TEC measurements.

Efficient data assimilation algorithm for bathymetry application

NASA Astrophysics Data System (ADS)

Ghorbanidehno, H.; Lee, J. H.; Farthing, M.; Hesser, T.; Kitanidis, P. K.; Darve, E. F.

2017-12-01

Information on the evolving state of the nearshore zone bathymetry is crucial to shoreline management, recreational safety, and naval operations. The high cost and complex logistics of using ship-based surveys for bathymetry estimation have encouraged the use of remote sensing techniques. Data assimilation methods combine the remote sensing data and nearshore hydrodynamic models to estimate the unknown bathymetry and the corresponding uncertainties. In particular, several recent efforts have combined Kalman Filter-based techniques such as ensembled-based Kalman filters with indirect video-based observations to address the bathymetry inversion problem. However, these methods often suffer from ensemble collapse and uncertainty underestimation. Here, the Compressed State Kalman Filter (CSKF) method is used to estimate the bathymetry based on observed wave celerity. In order to demonstrate the accuracy and robustness of the CSKF method, we consider twin tests with synthetic observations of wave celerity, while the bathymetry profiles are chosen based on surveys taken by the U.S. Army Corps of Engineer Field Research Facility (FRF) in Duck, NC. The first test case is a bathymetry estimation problem for a spatially smooth and temporally constant bathymetry profile. The second test case is a bathymetry estimation problem for a temporally evolving bathymetry from a smooth to a non-smooth profile. For both problems, we compare the results of CSKF with those obtained by the local ensemble transform Kalman filter (LETKF), which is a popular ensemble-based Kalman filter method.

Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing

NASA Astrophysics Data System (ADS)

Toye, Habib; Zhan, Peng; Gopalakrishnan, Ganesh; Kartadikaria, Aditya R.; Huang, Huang; Knio, Omar; Hoteit, Ibrahim

2017-07-01

We present our efforts to build an ensemble data assimilation and forecasting system for the Red Sea. The system consists of the high-resolution Massachusetts Institute of Technology general circulation model (MITgcm) to simulate ocean circulation and of the Data Research Testbed (DART) for ensemble data assimilation. DART has been configured to integrate all members of an ensemble adjustment Kalman filter (EAKF) in parallel, based on which we adapted the ensemble operations in DART to use an invariant ensemble, i.e., an ensemble Optimal Interpolation (EnOI) algorithm. This approach requires only single forward model integration in the forecast step and therefore saves substantial computational cost. To deal with the strong seasonal variability of the Red Sea, the EnOI ensemble is then seasonally selected from a climatology of long-term model outputs. Observations of remote sensing sea surface height (SSH) and sea surface temperature (SST) are assimilated every 3 days. Real-time atmospheric fields from the National Center for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF) are used as forcing in different assimilation experiments. We investigate the behaviors of the EAKF and (seasonal-) EnOI and compare their performances for assimilating and forecasting the circulation of the Red Sea. We further assess the sensitivity of the assimilation system to various filtering parameters (ensemble size, inflation) and atmospheric forcing.

Decision Support Systems To Manage Water Resources At Irrigation District Level In Southern Italy Using Remote Sensing Information. An Integrated Project (AQUATER)

NASA Astrophysics Data System (ADS)

Rinaldi, M.; Castrignanò, A.; Mastrorilli, M.; Rana, G.; Ventrella, D.; Acutis, M.; D'Urso, G.; Mattia, F.

2006-08-01

An efficient management of water resources is crucial point for Italy and in particular for southern areas characterized by Mediterranean climate in order to improve the economical and environmental sustainability of the agricultural activity. A three-year Project (2005-2008) has been funded by the Italian Ministry of Agriculture and Forestry Policies; it involves four Italian research institutions: the Agricultural Research Council (ISA, Bari), the National Research Council (ISSIA, Bari) and two Universities (Federico II-Naples and Milan). It is focused on the remote sensing, the plant and the climate and, for interdisciplinary relationships, the project working group consists of agronomists, engineers and physicists. The aims of the Project are: a) to produce a Decision Support System (DSS) combining remote sensing information, spatial data and simulation models to manage water resources in irrigation districts; b) to simulate irrigation scenarios to evaluate the effects of water stress on crop yield using agro-ecological indicators; c) to identify the most sensitive areas to drought risk in Southern Italy. The tools used in this Project will be: 1. Remote sensing images, topographic maps, soil and land use maps; 2. Geographic Information Systems; 3. Geostatistic methodologies; 4. Ground truth measurements (land use, canopy and soil temperatures, soil and plant water status, Normalized Difference Vegetation Index, Crop Water Stress Index, Leaf Area Index, actual evapotranspiration, crop coefficients, crop yield, agro-ecological indicators); 5. Crop simulation models. The Project is structured in four work packages with specific objectives, high degree of interaction and information exchange: 1) Remote Sensing and Image Analysis; 2) Cropping Systems; 3) Modelling and Softwares Development; 4) Stakeholders. The final product will be a DSS with the purpose of integrating remote sensing images, to estimate crop and soil variables related to drought, to assimilate these variables into a simulation model at district scale and, finally, to estimate evapotranspiration, plant water status and drought indicators. A project Web home page, a technical course about DSS for the employers of irrigation authorities and dissemination of results (meetings, publications, reports), are also planned.

The benefits of using remotely sensed soil moisture in parameter identification of large-scale hydrological models

NASA Astrophysics Data System (ADS)

Karssenberg, D.; Wanders, N.; de Roo, A.; de Jong, S.; Bierkens, M. F.

2013-12-01

Large-scale hydrological models are nowadays mostly calibrated using observed discharge. As a result, a large part of the hydrological system that is not directly linked to discharge, in particular the unsaturated zone, remains uncalibrated, or might be modified unrealistically. Soil moisture observations from satellites have the potential to fill this gap, as these provide the closest thing to a direct measurement of the state of the unsaturated zone, and thus are potentially useful in calibrating unsaturated zone model parameters. This is expected to result in a better identification of the complete hydrological system, potentially leading to improved forecasts of the hydrograph as well. Here we evaluate this added value of remotely sensed soil moisture in calibration of large-scale hydrological models by addressing two research questions: 1) Which parameters of hydrological models can be identified by calibration with remotely sensed soil moisture? 2) Does calibration with remotely sensed soil moisture lead to an improved calibration of hydrological models compared to approaches that calibrate only with discharge, such that this leads to improved forecasts of soil moisture content and discharge as well? To answer these questions we use a dual state and parameter ensemble Kalman filter to calibrate the hydrological model LISFLOOD for the Upper Danube area. Calibration is done with discharge and remotely sensed soil moisture acquired by AMSR-E, SMOS and ASCAT. Four scenarios are studied: no calibration (expert knowledge), calibration on discharge, calibration on remote sensing data (three satellites) and calibration on both discharge and remote sensing data. Using a split-sample approach, the model is calibrated for a period of 2 years and validated for the calibrated model parameters on a validation period of 10 years. Results show that calibration with discharge data improves the estimation of groundwater parameters (e.g., groundwater reservoir constant) and routing parameters. Calibration with only remotely sensed soil moisture results in an accurate calibration of parameters related to land surface process (e.g., the saturated conductivity of the soil), which is not possible when calibrating on discharge alone. For the upstream area up to 40000 km2, calibration on both discharge and soil moisture results in a reduction by 10-30 % in the RMSE for discharge simulations, compared to calibration on discharge alone. For discharge in the downstream area, the model performance due to assimilation of remotely sensed soil moisture is not increased or slightly decreased, most probably due to the longer relative importance of the routing and contribution of groundwater in downstream areas. When microwave soil moisture is used for calibration the RMSE of soil moisture simulations decreases from 0.072 m3m-3 to 0.062 m3m-3. The conclusion is that remotely sensed soil moisture holds potential for calibration of hydrological models leading to a better simulation of soil moisture content throughout and a better simulation of discharge in upstream areas, particularly if discharge observations are sparse.

On the assimilation of SWOT type data into 2D shallow-water models

NASA Astrophysics Data System (ADS)

Frédéric, Couderc; Denis, Dartus; Pierre-André, Garambois; Ronan, Madec; Jérôme, Monnier; Jean-Paul, Villa

2013-04-01

In river hydraulics, assimilation of water level measurements at gauging stations is well controlled, while assimilation of images is still delicate. In the present talk, we address the richness of satellite mapped information to constrain a 2D shallow-water model, but also related difficulties. 2D shallow models may be necessary for small scale modelling in particular for low-water and flood plain flows. Since in both cases, the dynamics of the wet-dry front is essential, one has to elaborate robust and accurate solvers. In this contribution we introduce robust second order, stable finite volume scheme [CoMaMoViDaLa]. Comparisons of real like tests cases with more classical solvers highlight the importance of an accurate flood plain modelling. A preliminary inverse study is presented in a flood plain flow case, [LaMo] [HoLaMoPu]. As a first step, a 0th order data processing model improves observation operator and produces more reliable water level derived from rough measurements [PuRa]. Then, both model and flow behaviours can be better understood thanks to variational sensitivities based on a gradient computation and adjoint equations. It can reveal several difficulties that a model designer has to tackle. Next, a 4D-Var data assimilation algorithm used with spatialized data leads to improved model calibration and potentially leads to identify river discharges. All the algorithms are implemented into DassFlow software (Fortran, MPI, adjoint) [Da]. All these results and experiments (accurate wet-dry front dynamics, sensitivities analysis, identification of discharges and calibration of model) are currently performed in view to use data from the future SWOT mission. [CoMaMoViDaLa] F. Couderc, R. Madec, J. Monnier, J.-P. Vila, D. Dartus, K. Larnier. "Sensitivity analysis and variational data assimilation for geophysical shallow water flows". Submitted. [Da] DassFlow - Data Assimilation for Free Surface Flows. Computational software http://www-gmm.insa-toulouse.fr/~monnier/DassFlow/ [HoLaMoPu] R. Hostache, X. Lai, J. Monnier, C. Puech. "Assimilation of spatial distributed water levels into a shallow-water flood model. Part II: using a remote sensing image of Mosel river". J. Hydrology (2010). [LaMo] X. Lai, J. Monnier. "Assimilation of spatial distributed water levels into a shallow-water flood model. Part I: mathematical method and test case". J. Hydrology (2009). [PuRa] C. Puech, D. Raclot. "Using geographic information systems and aerial photographs to determine water levels during floods". Hydrol. Process., 16, 1593 - 1602, (2002). [RoDa] H. Roux, D. Dartus. "Use of Parameter Optimization to Estimate a Flood Wave: Potential Applications to Remote Sensing of Rivers". J. Hydrology (2006).

Passive Microwave Remote Sensing of Soil Moisture

NASA Technical Reports Server (NTRS)

Njoku, Eni G.; Entekhabi, Dara

1996-01-01

Microwave remote sensing provides a unique capability for direct observation of soil moisture. Remote measurements from space afford the possibility of obtaining frequent, global sampling of soil moisture over a large fraction of the Earth's land surface. Microwave measurements have the benefit of being largely unaffected by cloud cover and variable surface solar illumination, but accurate soil moisture estimates are limited to regions that have either bare soil or low to moderate amounts of vegetation cover. A particular advantage of passive microwave sensors is that in the absence of significant vegetation cover soil moisture is the dominant effect on the received signal. The spatial resolutions of passive Microwave soil moisture sensors currently considered for space operation are in the range 10-20 km. The most useful frequency range for soil moisture sensing is 1-5 GHz. System design considerations include optimum choice of frequencies, polarizations, and scanning configurations, based on trade-offs between requirements for high vegetation penetration capability, freedom from electromagnetic interference, manageable antenna size and complexity, and the requirement that a sufficient number of information channels be available to correct for perturbing geophysical effects. This paper outlines the basic principles of the passive microwave technique for soil moisture sensing, and reviews briefly the status of current retrieval methods. Particularly promising are methods for optimally assimilating passive microwave data into hydrologic models. Further studies are needed to investigate the effects on microwave observations of within-footprint spatial heterogeneity of vegetation cover and subsurface soil characteristics, and to assess the limitations imposed by heterogeneity on the retrievability of large-scale soil moisture information from remote observations.

Assimilation of Remotely-Sensed Snow information to improve streamflow predictions in the Southwestern US

NASA Astrophysics Data System (ADS)

López-Burgos, V.; Rajagopal, S.; Martinez Baquero, G. F.; Gupta, H. V.

2009-12-01

Rapidly growing population in the southwestern US is leading to increasing demand and decreasing availability of water, requiring a detailed quantification of hydrological processes. The integration of detailed spatial information of water fluxes from remote sensing platforms, and hydrological models coupled with ground based data is an important step towards this goal. This project is exploring the use of Snow Water Equivalent (SWE) estimates to update the snow component of the Variable Infiltration Capacity model (VIC). SWE estimates are obtained by combining SNOTEL data with MODIS Snow Cover Area (SCA) information. Because, cloud cover corrupts the estimates of SCA, a rule-based method is used to clean up the remotely sensed images. The rules include a time interpolation method, and the probability of a pixel for been covered with snow based on the relationships between elevation, temperature, lapse rate, aspect and topographic shading. The approach is used to improve streamflow predictions on two rivers managed by the Salt River Project, a water and energy supplier in central Arizona. This solution will help improve the management of reservoirs in the Salt and Verde River in Phoenix, Arizona (tributaries of the lower Colorado River basin), by incorporating physically based distributed models and remote sensing observations into their Decision Support Tools and planning tools. This research seeks to increase the knowledge base used to manage reservoirs and groundwater resources in a region affected by a long-term drought. It will be applicable and relevant for other water utility companies facing the challenges of climate change and decreasing water resources.

Skin Temperature Analysis and Bias Correction in a Coupled Land-Atmosphere Data Assimilation System

NASA Technical Reports Server (NTRS)

Bosilovich, Michael G.; Radakovich, Jon D.; daSilva, Arlindo; Todling, Ricardo; Verter, Frances

2006-01-01

In an initial investigation, remotely sensed surface temperature is assimilated into a coupled atmosphere/land global data assimilation system, with explicit accounting for biases in the model state. In this scheme, an incremental bias correction term is introduced in the model's surface energy budget. In its simplest form, the algorithm estimates and corrects a constant time mean bias for each gridpoint; additional benefits are attained with a refined version of the algorithm which allows for a correction of the mean diurnal cycle. The method is validated against the assimilated observations, as well as independent near-surface air temperature observations. In many regions, not accounting for the diurnal cycle of bias caused degradation of the diurnal amplitude of background model air temperature. Energy fluxes collected through the Coordinated Enhanced Observing Period (CEOP) are used to more closely inspect the surface energy budget. In general, sensible heat flux is improved with the surface temperature assimilation, and two stations show a reduction of bias by as much as 30 Wm(sup -2) Rondonia station in Amazonia, the Bowen ratio changes direction in an improvement related to the temperature assimilation. However, at many stations the monthly latent heat flux bias is slightly increased. These results show the impact of univariate assimilation of surface temperature observations on the surface energy budget, and suggest the need for multivariate land data assimilation. The results also show the need for independent validation data, especially flux stations in varied climate regimes.

The Effects of Chlorophyll Assimilation on Carbon Fluxes in a Global Biogeochemical Model. [Technical Report Series on Global Modeling and Data Assimilation

NASA Technical Reports Server (NTRS)

Koster, Randal D. (Editor); Rousseaux, Cecile Severine; Gregg, Watson W.

2014-01-01

In this paper, we investigated whether the assimilation of remotely-sensed chlorophyll data can improve the estimates of air-sea carbon dioxide fluxes (FCO2). Using a global, established biogeochemical model (NASA Ocean Biogeochemical Model, NOBM) for the period 2003-2010, we found that the global FCO2 values produced in the free-run and after assimilation were within -0.6 mol C m(sup -2) y(sup -1) of the observations. The effect of satellite chlorophyll assimilation was assessed in 12 major oceanographic regions. The region with the highest bias was the North Atlantic. Here the model underestimated the fluxes by 1.4 mol C m(sup -2) y(sup -1) whereas all the other regions were within 1 mol C m(sup -2) y(sup -1) of the data. The FCO2 values were not strongly impacted by the assimilation, and the uncertainty in FCO2 was not decreased, despite the decrease in the uncertainty in chlorophyll concentration. Chlorophyll concentrations were within approximately 25% of the database in 7 out of the 12 regions, and the assimilation improved the chlorophyll concentration in the regions with the highest bias by 10-20%. These results suggest that the assimilation of chlorophyll data does not considerably improve FCO2 estimates and that other components of the carbon cycle play a role that could further improve our FCO2 estimates.

Preliminary Results from an Assimilation of TOMS Aerosol Observations Into the GOCART Model

NASA Technical Reports Server (NTRS)

daSilva, Arlindo; Weaver, Clark J.; Ginoux, Paul; Torres, Omar; Einaudi, Franco (Technical Monitor)

2000-01-01

At NASA Goddard we are developing a global aerosol data assimilation system that combines advances in remote sensing and modeling of atmospheric aerosols. The goal is to provide high resolution, 3-D aerosol distributions to the research community. Our first step is to develop a simple assimilation system for Saharan mineral aerosol. The Goddard Chemistry and Aerosol Radiation model (GOCART) provides accurate 3-D mineral aerosol size distributions that compare well with TOMS satellite observations. Surface, mobilization, wet and dry deposition, convective and long-range transport are all driven by assimilated fields from the Goddard Earth Observing System Data Assimilation System, GEOS-DAS. Our version of GOCART transports sizes from.08-10 microns and only simulates Saharan dust. TOMS radiance observations in the ultra violet provide information on the mineral and carbonaceous aerosol fields. We use two main observables in this study: the TOMS aerosol index (AI) which is directly related to the ratio of the 340 and 380 radiances and the 380 radiance. These are sensitive to the aerosol optical thickness, the single scattering albedo and the height of the aerosol layer. The Goddard Aerosol Assimilation System (GAAS) uses the Data Assimilation Office's Physical-space Statistical Analysis System (PSAS) to combine TOMS observations and GOCART model first guess fields. At this initial phase we only assimilate observations into the the GOCART model over regions of Africa and the Atlantic where mineral aerosols dominant and carbonaceous aerosols are minimal, Our preliminary results during summer show that the assimilation with TOMS data modifies both the aerosol mass loading and the single scattering albedo. Assimilated aerosol fields will be compared with assimilated aerosol fields from GOCART and AERONET observations over Cape Verde.

Effects of sounding temperature assimilation on weather forecasting - Model dependence studies

NASA Technical Reports Server (NTRS)

Ghil, M.; Halem, M.; Atlas, R.

1979-01-01

In comparing various methods for the assimilation of remote sounding information into numerical weather prediction (NWP) models, the problem of model dependence for the different results obtained becomes important. The paper investigates two aspects of the model dependence question: (1) the effect of increasing horizontal resolution within a given model on the assimilation of sounding data, and (2) the effect of using two entirely different models with the same assimilation method and sounding data. Tentative conclusions reached are: first, that model improvement as exemplified by increased resolution, can act in the same direction as judicious 4-D assimilation of remote sounding information, to improve 2-3 day numerical weather forecasts. Second, that the time continuous 4-D methods developed at GLAS have similar beneficial effects when used in the assimilation of remote sounding information into NWP models with very different numerical and physical characteristics.

Improving Global Modeling and Data Analysis Using Remotely-Sensed Rainfall Data: Lessons From TRMM and Plans for GPM

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.; Einaudi, Franco (Technical Monitor)

2001-01-01

I will discuss the need for accurate rainfall observations to improve our ability to model the earth's climate and improve short-range weather forecasts. I will give an overview of the recent progress in using of rainfall data provided by TRMM and other microwave instruments in data assimilation to improve global analyses and diagnose state-dependent systematic errors in physical parameterizations. I will outline the current and future research strategies in preparation for the Global Precipitation Mission.

Multi-parametric variational data assimilation for hydrological forecasting

NASA Astrophysics Data System (ADS)

Alvarado-Montero, R.; Schwanenberg, D.; Krahe, P.; Helmke, P.; Klein, B.

2017-12-01

Ensemble forecasting is increasingly applied in flow forecasting systems to provide users with a better understanding of forecast uncertainty and consequently to take better-informed decisions. A common practice in probabilistic streamflow forecasting is to force deterministic hydrological model with an ensemble of numerical weather predictions. This approach aims at the representation of meteorological uncertainty but neglects uncertainty of the hydrological model as well as its initial conditions. Complementary approaches use probabilistic data assimilation techniques to receive a variety of initial states or represent model uncertainty by model pools instead of single deterministic models. This paper introduces a novel approach that extends a variational data assimilation based on Moving Horizon Estimation to enable the assimilation of observations into multi-parametric model pools. It results in a probabilistic estimate of initial model states that takes into account the parametric model uncertainty in the data assimilation. The assimilation technique is applied to the uppermost area of River Main in Germany. We use different parametric pools, each of them with five parameter sets, to assimilate streamflow data, as well as remotely sensed data from the H-SAF project. We assess the impact of the assimilation in the lead time performance of perfect forecasts (i.e. observed data as forcing variables) as well as deterministic and probabilistic forecasts from ECMWF. The multi-parametric assimilation shows an improvement of up to 23% for CRPS performance and approximately 20% in Brier Skill Scores with respect to the deterministic approach. It also improves the skill of the forecast in terms of rank histogram and produces a narrower ensemble spread.

Assimilation of Leaf Area Index and Soil Wetness Index into the ISBA-A-gs land surface model over France

NASA Astrophysics Data System (ADS)

Barbu, A. L.; Calvet, J.-C.; Lafont, S.

2012-04-01

The development of a Land Data Assimilation System (LDAS) dedicated to carbon and water cycles is considered as a key aspect for monitoring activities of terrestrial carbon fluxes. It allows the assimilation of biophysical products in order to reduce the bias between the model simulations and the observations and have a positive impact on carbon and water fluxes. This work shows the benefits of data assimilation of Earth observations for the monitoring of vegetation status and carbon fluxes, in the framework of the GEOLAND2 project, co-funded by the European Commission within the GMES initiative in FP7. In this study, the SURFEX modelling platform developed at Meteo-France is used for describing the continental vegetation state, surface fluxes and soil moisture. It consists of the land surface model ISBA-A-gs that simulates photosynthesis and plant growth. The vegetation biomass and Leaf Area Index (LAI) evolve dynamically in response to weather and climate conditions. The ECOCLIMAP database provides detailed information about the land cover at a resolution of 1 km. Over the France domain, the most present ecosystem types are grasslands (32%), C3 crop lands (24%), deciduous forest (20%), bare soil (11%), and C4 crop lands (8%).The model also includes a representation of the soil moisture stress with two different types of drought responses for herbaceous vegetation and forests. A version of the Extended Kalman Filter (EKF) scheme is developed for the joint assimilation of satellite-derived surface soil moisture from ASCAT-25 km product, namely Soil Wetness Index (SWI-01) developed by TU-Wien, and remote sensing LAI product provided by GEOLAND2. The GEOLAND2 LAI product is derived from CYCLOPES V3.1 and MODIS collection 5 data. It is more consistent with an effective LAI for low LAI and close to the actual LAI for high values. The assimilation experiment was conducted across France at a spatial resolution of 8 km. The study period ranges from July 2007 to December 2010. In the model simulation, the start of the growing season tends to occur later than in the observations. Similarly, the senescence phase is delayed. The assimilation is able to reduce this bias. The lack of detailed knowledge of the farming practices and other shortcomings of the model are compensated by the assimilation of the remotely sensed LAI. The analyzed seasonal LAI cycle across large cropland regions (north-eastern France) is closer to the observations. A coherent impact of LAI and soil moisture updates on the carbon fluxes is noticed. Increased LAI values in the growing season due to data assimilation corrections trigger an increased photosynthetic activity. Similarly, lower LAI values corresponding to the senescence phase cause a decrease in the carbon dioxide uptake when compared to the original model simulations.

Assimilating uncertain, dynamic and intermittent streamflow observations in hydrological models

NASA Astrophysics Data System (ADS)

Mazzoleni, Maurizio; Alfonso, Leonardo; Chacon-Hurtado, Juan; Solomatine, Dimitri

2015-09-01

Catastrophic floods cause significant socio-economical losses. Non-structural measures, such as real-time flood forecasting, can potentially reduce flood risk. To this end, data assimilation methods have been used to improve flood forecasts by integrating static ground observations, and in some cases also remote sensing observations, within water models. Current hydrologic and hydraulic research works consider assimilation of observations coming from traditional, static sensors. At the same time, low-cost, mobile sensors and mobile communication devices are becoming also increasingly available. The main goal and innovation of this study is to demonstrate the usefulness of assimilating uncertain streamflow observations that are dynamic in space and intermittent in time in the context of two different semi-distributed hydrological model structures. The developed method is applied to the Brue basin, where the dynamic observations are imitated by the synthetic observations of discharge. The results of this study show how model structures and sensors locations affect in different ways the assimilation of streamflow observations. In addition, it proves how assimilation of such uncertain observations from dynamic sensors can provide model improvements similar to those of streamflow observations coming from a non-optimal network of static physical sensors. This can be a potential application of recent efforts to build citizen observatories of water, which can make the citizens an active part in information capturing, evaluation and communication, helping simultaneously to improvement of model-based flood forecasting.

Diagnostics of sources of tropospheric ozone using data assimilation during the KORUS-AQ campaign

NASA Astrophysics Data System (ADS)

Gaubert, B.; Emmons, L. K.; Miyazaki, K.; Buchholz, R. R.; Tang, W.; Arellano, A. F., Jr.; Tilmes, S.; Barré, J.; Worden, H. M.; Raeder, K.; Anderson, J. L.; Edwards, D. P.

2017-12-01

Atmospheric oxidative capacity plays a crucial role in the fate of greenhouse gases and air pollutants as well as in the formation of secondary pollutants such as tropospheric ozone. The attribution of sources of tropospheric ozone is a difficult task because of biases in input parameters and forcings such as emissions and meteorology in addition to errors in chemical schemes. We assimilate satellite remote sensing observations of ozone precursors such as carbon monoxide (CO) and nitrogen dioxide (NO2) in the global coupled chemistry-transport model: Community Atmosphere Model with Chemistry (CAM-Chem). The assimilation is completed using an Ensemble Adjustment Kalman Filter (EAKF) in the Data Assimilation Research Testbed (DART) framework which allows estimates of unobserved parameters and potential constraints on secondary pollutants and emissions. The ensemble will be constructed using perturbations in chemical kinetics, different emission fields and by assimilating meteorological observations to fully assess uncertainties in the chemical fields of targeted species. We present a set of tools such as emission tags (CO and propane), combined with diagnostic analysis of chemical regimes and perturbation of emissions ratios to estimate a regional budget of primary and secondary pollutants in East Asia and their sensitivity to data assimilation. This study benefits from the large set of aircraft and ozonesonde in-situ observations from the Korea-United States Air Quality (KORUS-AQ) campaign that occurred in South Korea in May-June 2016.

The potential for geostationary remote sensing of NO2 to improve weather prediction

NASA Astrophysics Data System (ADS)

Liu, X.; Mizzi, A. P.; Anderson, J. L.; Fung, I. Y.; Cohen, R. C.

2016-12-01

Observations of surface winds remain sparse making it challenging to simulate and predict the weather in circumstances of light winds that are most important for poor air quality. Direct measurements of short-lived chemicals from space might be a solution to this challenge. Here we investigate the application of data assimilation of NO­2 columns as will be observed from geostationary orbit to improve predictions and retrospective analysis of surface wind fields. Specifically, synthetic NO2 observations are sampled from a "nature run (NR)" regarded as the true atmosphere. Then NO2 observations are assimilated using EAKF methods into a "control run (CR)" which differs from the NR in the wind field. Wind errors are generated by introducing (1) errors in the initial conditions, (2) creating a model error by using two different formulations for the planetary boundary layer, (3) and by combining both of these effects. The assimilation reduces wind errors by up to 50%, indicating the prospects for future geostationary atmospheric composition measurements to improve weather forecasting are substantial. We also examine the assimilation sensitivity to the data assimilation window length. We find that due to the temporal heterogeneity of wind errors, the success of this application favors chemical observations of high frequency, such as those from geostationary platform. We also show the potential to improve soil moisture field by assimilating NO­2 columns.

Improving the Forecast Accuracy of an Ocean Observation and Prediction System by Adaptive Control of the Sensor Network

NASA Astrophysics Data System (ADS)

Talukder, A.; Panangadan, A. V.; Blumberg, A. F.; Herrington, T.; Georgas, N.

2008-12-01

The New York Harbor Observation and Prediction System (NYHOPS) is a real-time, estuarine and coastal ocean observing and modeling system for the New York Harbor and surrounding waters. Real-time measurements from in-situ mobile and stationary sensors in the NYHOPS networks are assimilated into marine forecasts in order to reduce the discrepancy with ground truth. The forecasts are obtained from the ECOMSED hydrodynamic model, a shallow water derivative of the Princeton Ocean Model. Currently, all sensors in the NYHOPS system are operated in a fixed mode with uniform sampling rates. This technology infusion effort demonstrates the use of Model Predictive Control (MPC) to autonomously adapt the operation of both mobile and stationary sensors in response to changing events that are -automatically detected from the ECOMSED forecasts. The controller focuses sensing resources on those regions that are expected to be impacted by the detected events. The MPC approach involves formulating the problem of calculating the optimal sensor parameters as a constrained multi-objective optimization problem. We have developed an objective function that takes into account the spatiotemporal relationship of the in-situ sensor locations and the locations of events detected by the model. Experiments in simulation were carried out using data collected during a freshwater flooding event. The location of the resulting freshwater plume was calculated from the corresponding model forecasts and was used by the MPC controller to derive control parameters for the sensing assets. The operational parameters that are controlled include the sampling rates of stationary sensors, paths of unmanned underwater vehicles (UUVs), and data transfer routes between sensors and the central modeling computer. The simulation experiments show that MPC-based sensor control reduces the RMS error in the forecast by a factor of 380% as compared to uniform sampling. The paths of multiple UUVs were simultaneously calculated such that measurements from on-board sensors would lead to maximal reduction in the forecast error after data assimilation. The MPC controller also reduces the consumption of system resources such as energy expended in sampling and wireless communication. The MPC-based control approach can be generalized to accept data from remote sensing satellites. This will enable in-situ sensors to be regulated using forecasts generated by assimilating local high resolution in-situ measurements with wide-area observations from remote sensing satellites.

Dual Assimilation of Microwave and Thermal-Infrared Satellite Observations of Soil Moisture into NLDAS for Improved Drought Monitoring

NASA Astrophysics Data System (ADS)

Hain, C.; Crow, W. T.; Anderson, M. C.; Zhan, X.; Wardlow, B.; Svoboda, M. D.; Mecikalski, J. R.

2011-12-01

Our research group is currently developing an operational data assimilation (DA) system for the optimal assimilation of thermal infrared (TIR) and microwave (MV) soil moisture (SM) and insertion of near real-time green vegetation fraction (GVF) into the Noah land-surface model component of the National Land Data Assimilation System (NLDAS). NLDAS produces the hydrologic products (e.g. soil moisture, evapotranspiration, and runoff) used by NCEP for operational drought monitoring, but these products are sensitive to model input errors in soil texture (affecting infiltration rates) and prescribed precipitation rates. Periodic updates of SM state variables in LSMs achieved by assimilating diagnostic moisture information retrieved using satellite remote sensing have been shown to compensate for model errors and result in improved hydrologic output. The work proposed here will build on a project currently funded under the Climate Test Bed Program entitled "A GOES Thermal-Based Drought Early Warning Index for NIDIS", which is developing an operational TIR SM index (Evaporative Stress Index; ESI) based on maps of the ratio of actual to potential ET (fPET) generated with the Atmosphere-Land Exchange Inverse (ALEXI) surface energy balance algorithm. The research team has demonstrated that diagnostic information about SM and evapotranspiration (ET) from MW and TIR remote sensing can significantly reduce SM drifts in LSMs such as Noah. The two different SM retrievals have been shown to be quite complementary: TIR provides relatively high spatial (down to 100 m) and low temporal resolution (due to cloud cover) retrievals over a wide range of GVF, while MW provides relatively low spatial (25 to 60 km) and high temporal resolution (can retrieve through cloud cover), but only over areas with low GVF. Furthermore, MW retrievals are sensitive to SM only in the first few centimeters of the soil profile, while TIR provides information about SM conditions integrated over the full root-zone, reflected in the observed canopy temperature. The added value of TIR over MW alone is most significant in areas of moderate to dense vegetation cover where MW retrievals have very little sensitivity to SM at any depth. Finally, climatological estimates of GVF currently used in the operational NLDAS are not always representative of observed seasonal and intra-seasonal GVF conditions, especially in regions experiencing drought conditions. A detailed methodology of the assimilation system will be presented along with an analysis of initial results, with an emphasis on comparisons with in-situ SM observations and standard drought metrics.

Treatment of systematic errors in land data assimilation systems

NASA Astrophysics Data System (ADS)

Crow, W. T.; Yilmaz, M.

2012-12-01

Data assimilation systems are generally designed to minimize the influence of random error on the estimation of system states. Yet, experience with land data assimilation systems has also revealed the presence of large systematic differences between model-derived and remotely-sensed estimates of land surface states. Such differences are commonly resolved prior to data assimilation through implementation of a pre-processing rescaling step whereby observations are scaled (or non-linearly transformed) to somehow "match" comparable predictions made by an assimilation model. While the rationale for removing systematic differences in means (i.e., bias) between models and observations is well-established, relatively little theoretical guidance is currently available to determine the appropriate treatment of higher-order moments during rescaling. This talk presents a simple analytical argument to define an optimal linear-rescaling strategy for observations prior to their assimilation into a land surface model. While a technique based on triple collocation theory is shown to replicate this optimal strategy, commonly-applied rescaling techniques (e.g., so called "least-squares regression" and "variance matching" approaches) are shown to represent only sub-optimal approximations to it. Since the triple collocation approach is likely infeasible in many real-world circumstances, general advice for deciding between various feasible (yet sub-optimal) rescaling approaches will be presented with an emphasis of the implications of this work for the case of directly assimilating satellite radiances. While the bulk of the analysis will deal with linear rescaling techniques, its extension to nonlinear cases will also be discussed.

Evaluation of reported NOx emission trends between 2005 and 2013 by assimilation of OMI-NO2 data into LOTOS-EUROS

NASA Astrophysics Data System (ADS)

Schaap, Martijn; Segers, Arjo; Curier, Lyana; Timmermans, Renske

2016-04-01

Consistent and long time series of remotely sensed trace gas levels may provide a useful tool to estimate surface emissions and emission trends. We use the OMI-NO2 product in conjunction with the LOTOS-EUROS CTM to estimate European emission trends through correction of the OMI-time series for meteorological variability as well as through assimilation using an ensemble kalman filter system (EnKF). The chemistry transport model captures a large fraction of the variability in NO2 columns at a synoptic timescale, although a seasonal signal in the bias between the modeled and retrieved column data remains. Prior to the assimilation, the OMI-NO2 data have been analyzed to establish the spatially variable temporal and spatial correlation lengths, required for the settings in the EnKF system. The assimilation run for 2005-2013 was performed using constant 2005 emissions to be able to quantify the emission change. The assimilation reduces the model-observation differences considerably. Significant negative trends of 2-3 % per year (as compared to 2005) were found in highly industrialized areas across Western Europe. The assimilation system also identifies the areas with major emission reductions in e.g. northern Spain as identified in earlier studies. Comparison of the trends derived from the assimilation and the data itself shows a high level of agreement, both the trends found in this way are smaller than those reported.

Towards Year-round Estimation of Terrestrial Water Storage over Snow-Covered Terrain via Multi-sensor Assimilation of GRACE/GRACE-FO and AMSR-E/AMSR-2.

NASA Astrophysics Data System (ADS)

Wang, J.; Xue, Y.; Forman, B. A.; Girotto, M.; Reichle, R. H.

2017-12-01

The Gravity and Recovery Climate Experiment (GRACE) has revolutionized large-scale remote sensing of the Earth's terrestrial hydrologic cycle and has provided an unprecedented observational constraint for global land surface models. However, the coarse-scale (in space and time), vertically-integrated measure of terrestrial water storage (TWS) limits GRACE's applicability to smaller scale hydrologic applications. In order to enhance model-based estimates of TWS while effectively adding resolution (in space and time) to the coarse-scale TWS retrievals, a multi-variate, multi-sensor data assimilation framework is presented here that simultaneously assimilates gravimetric retrievals of TWS in conjunction with passive microwave (PMW) brightness temperature (Tb) observations over snow-covered terrain. The framework uses the NASA Catchment Land Surface Model (Catchment) and an ensemble Kalman filter (EnKF). A synthetic assimilation experiment is presented for the Volga river basin in Russia. The skill of the output from the assimilation of synthetic observations is compared with that of model estimates generated without the benefit of assimilating the synthetic observations. It is shown that the EnKF framework improves modeled estimates of TWS, snow depth, and snow mass (a.k.a. snow water equivalent). The data assimilation routine produces a conditioned (updated) estimate that is more accurate and contains less uncertainty during both the snow accumulation phase of the snow season as well as during the snow ablation season.

High resolution change estimation of soil moisture and its assimilation into a land surface model

NASA Astrophysics Data System (ADS)

Narayan, Ujjwal

Near surface soil moisture plays an important role in hydrological processes including infiltration, evapotranspiration and runoff. These processes depend non-linearly on soil moisture and hence sub-pixel scale soil moisture variability characterization is important for accurate modeling of water and energy fluxes at the pixel scale. Microwave remote sensing has evolved as an attractive technique for global monitoring of near surface soil moisture. A radiative transfer model has been tested and validated for soil moisture retrieval from passive microwave remote sensing data under a full range of vegetation water content conditions. It was demonstrated that soil moisture retrieval errors of approximately 0.04 g/g gravimetric soil moisture are attainable with vegetation water content as high as 5 kg/m2. Recognizing the limitation of low spatial resolution associated with passive sensors, an algorithm that uses low resolution passive microwave (radiometer) and high resolution active microwave (radar) data to estimate soil moisture change at the spatial resolution of radar operation has been developed and applied to coincident Passive and Active L and S band (PALS) and Airborne Synthetic Aperture Radar (AIRSAR) datasets acquired during the Soil Moisture Experiments in 2002 (SMEX02) campaign with root mean square error of 10% and a 4 times enhancement in spatial resolution. The change estimation algorithm has also been used to estimate soil moisture change at 5 km resolution using AMSR-E soil moisture product (50 km) in conjunction with the TRMM-PR data (5 km) for a 3 month period demonstrating the possibility of high resolution soil moisture change estimation using satellite based data. Soil moisture change is closely related to precipitation and soil hydraulic properties. A simple assimilation framework has been implemented to investigate whether assimilation of surface layer soil moisture change observations into a hydrologic model will potentially improve it performance. Results indicate an improvement in model prediction of near surface and deep layer soil moisture content when the update is performed to the model state as compared to free model runs. It is also seen that soil moisture change assimilation is able to mitigate the effect of erroneous precipitation input data.

The Rise of GNSS Reflectometry for Earth Remote Sensing

NASA Technical Reports Server (NTRS)

Zuffada, Cinzia; Li, Zhijin; Nghiem, Son V.; Lowe, Steve; Shah, Rashmi; Clarizia, Maria Paola; Cardellach, Estel

2015-01-01

The Global Navigation Satellite System (GNSS) reflectometry, i.e. GNSS-R, is a novel remote-sensing technique first published in that uses GNSS signals reflected from the Earth's surface to infer its surface properties such as sea surface height (SSH), ocean winds, sea-ice coverage, vegetation, wetlands and soil moisture, to name a few. This communication discusses the scientific value of GNSS-R to (a) furthering our understanding of ocean mesoscale circulation toward scales finer than those that existing nadir altimeters can resolve, and (b) mapping vegetated wetlands, an emerging application that might open up new avenues to map and monitor the planet's wetlands for methane emission assessments. Such applications are expected to be demonstrated by the availability of data from GEROS-ISS, an ESA experiment currently in phase A, and CyGNSS [3], a NASA mission currently in development. In particular, the paper details the expected error characteristics and the role of filtering played in the assimilation of these data to reduce the altimetric error (when averaging many measurements).

Applications of Satellite Remote Sensing Products to Enhance and Evaluate the AIRPACT Regional Air Quality Modeling System

NASA Astrophysics Data System (ADS)

Herron-Thorpe, F. L.; Mount, G. H.; Emmons, L. K.; Lamb, B. K.; Jaffe, D. A.; Wigder, N. L.; Chung, S. H.; Zhang, R.; Woelfle, M.; Vaughan, J. K.; Leung, F. T.

2013-12-01

The WSU AIRPACT air quality modeling system for the Pacific Northwest forecasts hourly levels of aerosols and atmospheric trace gases for use in determining potential health and ecosystem impacts by air quality managers. AIRPACT uses the WRF/SMOKE/CMAQ modeling framework, derives dynamic boundary conditions from MOZART-4 forecast simulations with assimilated MOPITT CO, and uses the BlueSky framework to derive fire emissions. A suite of surface measurements and satellite-based remote sensing data products across the AIRPACT domain are used to evaluate and improve model performance. Specific investigations include anthropogenic emissions, wildfire simulations, and the effects of long-range transport on surface ozone. In this work we synthesize results for multiple comparisons of AIRPACT with satellite products such as IASI ammonia, AIRS carbon monoxide, MODIS AOD, OMI tropospheric ozone and nitrogen dioxide, and MISR plume height. Features and benefits of the newest version of AIRPACT's web-interface are also presented.

Application of remote sensing in regional scale estimates of vegetation carbon budgets: The Belfix project

NASA Technical Reports Server (NTRS)

Veroustraete, Frank; Patyn, Johan; Myneni, R. B.

1994-01-01

A concept for coupling the remote sensing derived fraction of the absorbed photosynthetic active radiation (FAPAR) with a functional ecosystem model was developed. The study was named the Belfix procedure. The quantification of changes in carbon dynamics at the ecosystem level is a key issue in studies of global climatic change effects at the vegetation atmosphere interface. An operational procedure, for the determination of carbon fluxes at the regional scale (Belgian territory), is presented. The approach allows for the determination of the sink function of vegetation for carbon (dioxide). The phyto- and litter mass, photosynthetic assimilation, autotroph and heterotroph carbon fluxes and net ecosystem exchange (NEE) of carbon, were evaluated. The results suggest that a single solution can be obtained for ecosystem rates and states, applying an iterative procedure, based on minimizing the change in maximal seasonal green phytomass in function of yearly FAPAR temporal profiles. Total phytomass values obtained are in close range with those obtained by ground sampling.

Assimilation of the seabird and ship drift data in the north-eastern sea of Japan into an operational ocean nowcast/forecast system

PubMed Central

Miyazawa, Yasumasa; Guo, Xinyu; Varlamov, Sergey M.; Miyama, Toru; Yoda, Ken; Sato, Katsufumi; Kano, Toshiyuki; Sato, Keiji

2015-01-01

At the present time, ocean current is being operationally monitored mainly by combined use of numerical ocean nowcast/forecast models and satellite remote sensing data. Improvement in the accuracy of the ocean current nowcast/forecast requires additional measurements with higher spatial and temporal resolution as expected from the current observation network. Here we show feasibility of assimilating high-resolution seabird and ship drift data into an operational ocean forecast system. Data assimilation of geostrophic current contained in the observed drift leads to refinement in the gyre mode events of the Tsugaru warm current in the north-eastern sea of Japan represented by the model. Fitting the observed drift to the model depends on ability of the drift representing geostrophic current compared to that representing directly wind driven components. A preferable horizontal scale of 50 km indicated for the seabird drift data assimilation implies their capability of capturing eddies with smaller horizontal scale than the minimum scale of 100 km resolved by the satellite altimetry. The present study actually demonstrates that transdisciplinary approaches combining bio-/ship- logging and numerical modeling could be effective for enhancement in monitoring the ocean current. PMID:26633309

A Dynamic Approach to Addressing Observation-Minus-Forecast Mean Differences in a Land Surface Skin Temperature Data Assimilation System

NASA Technical Reports Server (NTRS)

Draper, Clara; Reichle, Rolf; De Lannoy, Gabrielle; Scarino, Benjamin

2015-01-01

In land data assimilation, bias in the observation-minus-forecast (O-F) residuals is typically removed from the observations prior to assimilation by rescaling the observations to have the same long-term mean (and higher-order moments) as the corresponding model forecasts. Such observation rescaling approaches require a long record of observed and forecast estimates, and an assumption that the O-F mean differences are stationary. A two-stage observation bias and state estimation filter is presented, as an alternative to observation rescaling that does not require a long data record or assume stationary O-F mean differences. The two-stage filter removes dynamic (nonstationary) estimates of the seasonal scale O-F mean difference from the assimilated observations, allowing the assimilation to correct the model for synoptic-scale errors without adverse effects from observation biases. The two-stage filter is demonstrated by assimilating geostationary skin temperature (Tsk) observations into the Catchment land surface model. Global maps of the O-F mean differences are presented, and the two-stage filter is evaluated for one year over the Americas. The two-stage filter effectively removed the Tsk O-F mean differences, for example the GOES-West O-F mean difference at 21:00 UTC was reduced from 5.1 K for a bias-blind assimilation to 0.3 K. Compared to independent in situ and remotely sensed Tsk observations, the two-stage assimilation reduced the unbiased Root Mean Square Difference (ubRMSD) of the modeled Tsk by 10 of the open-loop values.

Deriving temporally continuous soil moisture estimations at fine resolution by downscaling remotely sensed product

NASA Astrophysics Data System (ADS)

Jin, Yan; Ge, Yong; Wang, Jianghao; Heuvelink, Gerard B. M.

2018-06-01

Land surface soil moisture (SSM) has important roles in the energy balance of the land surface and in the water cycle. Downscaling of coarse-resolution SSM remote sensing products is an efficient way for producing fine-resolution data. However, the downscaling methods used most widely require full-coverage visible/infrared satellite data as ancillary information. These methods are restricted to cloud-free days, making them unsuitable for continuous monitoring. The purpose of this study is to overcome this limitation to obtain temporally continuous fine-resolution SSM estimations. The local spatial heterogeneities of SSM and multiscale ancillary variables were considered in the downscaling process both to solve the problem of the strong variability of SSM and to benefit from the fusion of ancillary information. The generation of continuous downscaled remote sensing data was achieved via two principal steps. For cloud-free days, a stepwise hybrid geostatistical downscaling approach, based on geographically weighted area-to-area regression kriging (GWATARK), was employed by combining multiscale ancillary variables with passive microwave remote sensing data. Then, the GWATARK-estimated SSM and China Soil Moisture Dataset from Microwave Data Assimilation SSM data were combined to estimate fine-resolution data for cloudy days. The developed methodology was validated by application to the 25-km resolution daily AMSR-E SSM product to produce continuous SSM estimations at 1-km resolution over the Tibetan Plateau. In comparison with ground-based observations, the downscaled estimations showed correlation (R ≥ 0.7) for both ascending and descending overpasses. The analysis indicated the high potential of the proposed approach for producing a temporally continuous SSM product at fine spatial resolution.

Reducing uncertainty for estimating forest carbon stocks and dynamics using integrated remote sensing, forest inventory and process-based modeling

NASA Astrophysics Data System (ADS)

Poulter, B.; Ciais, P.; Joetzjer, E.; Maignan, F.; Luyssaert, S.; Barichivich, J.

2015-12-01

Accurately estimating forest biomass and forest carbon dynamics requires new integrated remote sensing, forest inventory, and carbon cycle modeling approaches. Presently, there is an increasing and urgent need to reduce forest biomass uncertainty in order to meet the requirements of carbon mitigation treaties, such as Reducing Emissions from Deforestation and forest Degradation (REDD+). Here we describe a new parameterization and assimilation methodology used to estimate tropical forest biomass using the ORCHIDEE-CAN dynamic global vegetation model. ORCHIDEE-CAN simulates carbon uptake and allocation to individual trees using a mechanistic representation of photosynthesis, respiration and other first-order processes. The model is first parameterized using forest inventory data to constrain background mortality rates, i.e., self-thinning, and productivity. Satellite remote sensing data for forest structure, i.e., canopy height, is used to constrain simulated forest stand conditions using a look-up table approach to match canopy height distributions. The resulting forest biomass estimates are provided for spatial grids that match REDD+ project boundaries and aim to provide carbon estimates for the criteria described in the IPCC Good Practice Guidelines Tier 3 category. With the increasing availability of forest structure variables derived from high-resolution LIDAR, RADAR, and optical imagery, new methodologies and applications with process-based carbon cycle models are becoming more readily available to inform land management.

SMERGE: A multi-decadal root-zone soil moisture product for CONUS

NASA Astrophysics Data System (ADS)

Crow, W. T.; Dong, J.; Tobin, K. J.; Torres, R.

2017-12-01

Multi-decadal root-zone soil moisture products are of value for a range of water resource and climate applications. The NASA-funded root-zone soil moisture merging project (SMERGE) seeks to develop such products through the optimal merging of land surface model predictions with surface soil moisture retrievals acquired from multi-sensor remote sensing products. This presentation will describe the creation and validation of a daily, multi-decadal (1979-2015), vertically-integrated (both surface to 40 cm and surface to 100 cm), 0.125-degree root-zone product over the contiguous United States (CONUS). The modeling backbone of the system is based on hourly root-zone soil moisture simulations generated by the Noah model (v3.2) operating within the North American Land Data Assimilation System (NLDAS-2). Remotely-sensed surface soil moisture retrievals are taken from the multi-sensor European Space Agency Climate Change Initiative soil moisture data set (ESA CCI SM). In particular, the talk will detail: 1) the exponential smoothing approach used to convert surface ESA CCI SM retrievals into root-zone soil moisture estimates, 2) the averaging technique applied to merge (temporally-sporadic) remotely-sensed with (continuous) NLDAS-2 land surface model estimates of root-zone soil moisture into the unified SMERGE product, and 3) the validation of the SMERGE product using long-term, ground-based soil moisture datasets available within CONUS.

The Goddard Snow Radiance Assimilation Project: An Integrated Snow Radiance and Snow Physics Modeling Framework for Snow/cold Land Surface Modeling

NASA Technical Reports Server (NTRS)

Kim, E.; Tedesco, M.; Reichle, R.; Choudhury, B.; Peters-Lidard C.; Foster, J.; Hall, D.; Riggs, G.

2006-01-01

Microwave-based retrievals of snow parameters from satellite observations have a long heritage and have so far been generated primarily by regression-based empirical "inversion" methods based on snapshots in time. Direct assimilation of microwave radiance into physical land surface models can be used to avoid errors associated with such retrieval/inversion methods, instead utilizing more straightforward forward models and temporal information. This approach has been used for years for atmospheric parameters by the operational weather forecasting community with great success. Recent developments in forward radiative transfer modeling, physical land surface modeling, and land data assimilation are converging to allow the assembly of an integrated framework for snow/cold lands modeling and radiance assimilation. The objective of the Goddard snow radiance assimilation project is to develop such a framework and explore its capabilities. The key elements of this framework include: a forward radiative transfer model (FRTM) for snow, a snowpack physical model, a land surface water/energy cycle model, and a data assimilation scheme. In fact, multiple models are available for each element enabling optimization to match the needs of a particular study. Together these form a modular and flexible framework for self-consistent, physically-based remote sensing and water/energy cycle studies. In this paper we will describe the elements and the integration plan. All modules will operate within the framework of the Land Information System (LIS), a land surface modeling framework with data assimilation capabilities running on a parallel-node computing cluster. Capabilities for assimilation of snow retrieval products are already under development for LIS. We will describe plans to add radiance-based assimilation capabilities. Plans for validation activities using field measurements will also be discussed.

Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems

NASA Astrophysics Data System (ADS)

Scholze, Marko; Buchwitz, Michael; Dorigo, Wouter; Guanter, Luis; Quegan, Shaun

2017-07-01

The global carbon cycle is an important component of the Earth system and it interacts with the hydrology, energy and nutrient cycles as well as ecosystem dynamics. A better understanding of the global carbon cycle is required for improved projections of climate change including corresponding changes in water and food resources and for the verification of measures to reduce anthropogenic greenhouse gas emissions. An improved understanding of the carbon cycle can be achieved by data assimilation systems, which integrate observations relevant to the carbon cycle into coupled carbon, water, energy and nutrient models. Hence, the ingredients for such systems are a carbon cycle model, an algorithm for the assimilation and systematic and well error-characterised observations relevant to the carbon cycle. Relevant observations for assimilation include various in situ measurements in the atmosphere (e.g. concentrations of CO2 and other gases) and on land (e.g. fluxes of carbon water and energy, carbon stocks) as well as remote sensing observations (e.g. atmospheric composition, vegetation and surface properties).We briefly review the different existing data assimilation techniques and contrast them to model benchmarking and evaluation efforts (which also rely on observations). A common requirement for all assimilation techniques is a full description of the observational data properties. Uncertainty estimates of the observations are as important as the observations themselves because they similarly determine the outcome of such assimilation systems. Hence, this article reviews the requirements of data assimilation systems on observations and provides a non-exhaustive overview of current observations and their uncertainties for use in terrestrial carbon cycle data assimilation. We report on progress since the review of model-data synthesis in terrestrial carbon observations by Raupach et al.(2005), emphasising the rapid advance in relevant space-based observations.

Joint Center for Satellite Data Assimilation Overview and Research Activities

NASA Astrophysics Data System (ADS)

Auligne, T.

2017-12-01

In 2001 NOAA/NESDIS, NOAA/NWS, NOAA/OAR, and NASA, subsequently joined by the US Navy and Air Force, came together to form the Joint Center for Satellite Data Assimilation (JCSDA) for the common purpose of accelerating the use of satellite data in environmental numerical prediction modeling by developing, using, and anticipating advances in numerical modeling, satellite-based remote sensing, and data assimilation methods. The primary focus was to bring these advances together to improve operational numerical model-based forecasting, under the premise that these partners have common technical and logistical challenges assimilating satellite observations into their modeling enterprises that could be better addressed through cooperative action and/or common solutions. Over the last 15 years, the JCSDA has made and continues to make major contributions to operational assimilation of satellite data. The JCSDA is a multi-agency U.S. government-owned-and-operated organization that was conceived as a venue for the several agencies NOAA, NASA, USAF and USN to collaborate on advancing the development and operational use of satellite observations into numerical model-based environmental analysis and forecasting. The primary mission of the JCSDA is to "accelerate and improve the quantitative use of research and operational satellite data in weather, ocean, climate and environmental analysis and prediction systems." This mission is fulfilled through directed research targeting the following key science objectives: Improved radiative transfer modeling; new instrument assimilation; assimilation of humidity, clouds, and precipitation observations; assimilation of land surface observations; assimilation of ocean surface observations; atmospheric composition; and chemistry and aerosols. The goal of this presentation is to briefly introduce the JCSDA's mission and vision, and to describe recent research activities across various JCSDA partners.

Assimilation of ASCAT near-surface soil moisture into the French SIM hydrological model

NASA Astrophysics Data System (ADS)

Draper, C.; Mahfouf, J.-F.; Calvet, J.-C.; Martin, E.; Wagner, W.

2011-06-01

The impact of assimilating near-surface soil moisture into the SAFRAN-ISBA-MODCOU (SIM) hydrological model over France is examined. Specifically, the root-zone soil moisture in the ISBA land surface model is constrained over three and a half years, by assimilating the ASCAT-derived surface degree of saturation product, using a Simplified Extended Kalman Filter. In this experiment ISBA is forced with the near-real time SAFRAN analysis, which analyses the variables required to force ISBA from relevant observations available before the real time data cut-off. The assimilation results are tested against ISBA forecasts generated with a higher quality delayed cut-off SAFRAN analysis. Ideally, assimilating the ASCAT data will constrain the ISBA surface state to correct for errors in the near-real time SAFRAN forcing, the most significant of which was a substantial dry bias caused by a dry precipitation bias. The assimilation successfully reduced the mean root-zone soil moisture bias, relative to the delayed cut-off forecasts, by close to 50 % of the open-loop value. The improved soil moisture in the model then led to significant improvements in the forecast hydrological cycle, reducing the drainage, runoff, and evapotranspiration biases (by 17 %, 11 %, and 70 %, respectively). When coupled to the MODCOU hydrogeological model, the ASCAT assimilation also led to improved streamflow forecasts, increasing the mean discharge ratio, relative to the delayed cut off forecasts, from 0.68 to 0.76. These results demonstrate that assimilating near-surface soil moisture observations can effectively constrain the SIM model hydrology, while also confirming the accuracy of the ASCAT surface degree of saturation product. This latter point highlights how assimilation experiments can contribute towards the difficult issue of validating remotely sensed land surface observations over large spatial scales.

Accounting for disturbance history in models: using remote sensing to constrain carbon and nitrogen pool spin-up.

PubMed

Hanan, Erin J; Tague, Christina; Choate, Janet; Liu, Mingliang; Kolden, Crystal; Adam, Jennifer

2018-03-24

Disturbances such as wildfire, insect outbreaks, and forest clearing, play an important role in regulating carbon, nitrogen, and hydrologic fluxes in terrestrial watersheds. Evaluating how watersheds respond to disturbance requires understanding mechanisms that interact over multiple spatial and temporal scales. Simulation modeling is a powerful tool for bridging these scales; however, model projections are limited by uncertainties in the initial state of plant carbon and nitrogen stores. Watershed models typically use one of two methods to initialize these stores: spin-up to steady state or remote sensing with allometric relationships. Spin-up involves running a model until vegetation reaches equilibrium based on climate. This approach assumes that vegetation across the watershed has reached maturity and is of uniform age, which fails to account for landscape heterogeneity and non-steady-state conditions. By contrast, remote sensing, can provide data for initializing such conditions. However, methods for assimilating remote sensing into model simulations can also be problematic. They often rely on empirical allometric relationships between a single vegetation variable and modeled carbon and nitrogen stores. Because allometric relationships are species- and region-specific, they do not account for the effects of local resource limitation, which can influence carbon allocation (to leaves, stems, roots, etc.). To address this problem, we developed a new initialization approach using the catchment-scale ecohydrologic model RHESSys. The new approach merges the mechanistic stability of spin-up with the spatial fidelity of remote sensing. It uses remote sensing to define spatially explicit targets for one or several vegetation state variables, such as leaf area index, across a watershed. The model then simulates the growth of carbon and nitrogen stores until the defined targets are met for all locations. We evaluated this approach in a mixed pine-dominated watershed in central Idaho, and a chaparral-dominated watershed in southern California. In the pine-dominated watershed, model estimates of carbon, nitrogen, and water fluxes varied among methods, while the target-driven method increased correspondence between observed and modeled streamflow. In the chaparral watershed, where vegetation was more homogeneously aged, there were no major differences among methods. Thus, in heterogeneous, disturbance-prone watersheds, the target-driven approach shows potential for improving biogeochemical projections. © 2018 by the Ecological Society of America.

A multi-source data assimilation framework for flood forecasting: Accounting for runoff routing lags

NASA Astrophysics Data System (ADS)

Meng, S.; Xie, X.

2015-12-01

In the flood forecasting practice, model performance is usually degraded due to various sources of uncertainties, including the uncertainties from input data, model parameters, model structures and output observations. Data assimilation is a useful methodology to reduce uncertainties in flood forecasting. For the short-term flood forecasting, an accurate estimation of initial soil moisture condition will improve the forecasting performance. Considering the time delay of runoff routing is another important effect for the forecasting performance. Moreover, the observation data of hydrological variables (including ground observations and satellite observations) are becoming easily available. The reliability of the short-term flood forecasting could be improved by assimilating multi-source data. The objective of this study is to develop a multi-source data assimilation framework for real-time flood forecasting. In this data assimilation framework, the first step is assimilating the up-layer soil moisture observations to update model state and generated runoff based on the ensemble Kalman filter (EnKF) method, and the second step is assimilating discharge observations to update model state and runoff within a fixed time window based on the ensemble Kalman smoother (EnKS) method. This smoothing technique is adopted to account for the runoff routing lag. Using such assimilation framework of the soil moisture and discharge observations is expected to improve the flood forecasting. In order to distinguish the effectiveness of this dual-step assimilation framework, we designed a dual-EnKF algorithm in which the observed soil moisture and discharge are assimilated separately without accounting for the runoff routing lag. The results show that the multi-source data assimilation framework can effectively improve flood forecasting, especially when the runoff routing has a distinct time lag. Thus, this new data assimilation framework holds a great potential in operational flood forecasting by merging observations from ground measurement and remote sensing retrivals.

The Hurricane-flood-landslide continuum-forecasting Hurricane effects at landfall

USGS Publications Warehouse

Negri, A.J.; Golden, J.H.; Updike, R.G.

2004-01-01

The integration of remote-sensing and in-situ observations, and assimilation of these observations into high-resolution mesoscale models was described. It was observed that the greatest loss of life and property is the direct result of the storm because of the high winds and heavy rain. The research were conducted to develop a warning system that included satellite observations to track dangerous level of precipitation and hurricane. The focus of the continuum project was to improve the ability to predict the evolution of the Earth system.

Stream Flow Prediction by Remote Sensing and Genetic Programming

NASA Technical Reports Server (NTRS)

Chang, Ni-Bin

2009-01-01

A genetic programming (GP)-based, nonlinear modeling structure relates soil moisture with synthetic-aperture-radar (SAR) images to present representative soil moisture estimates at the watershed scale. Surface soil moisture measurement is difficult to obtain over a large area due to a variety of soil permeability values and soil textures. Point measurements can be used on a small-scale area, but it is impossible to acquire such information effectively in large-scale watersheds. This model exhibits the capacity to assimilate SAR images and relevant geoenvironmental parameters to measure soil moisture.

An Observing System Simulation Experiment of assimilating leaf area index and soil moisture over cropland

NASA Astrophysics Data System (ADS)

Lafont, Sebastien; Barbu, Alina; Calvet, Jean-Christophe

2013-04-01

A Land Data Assimilation System (LDAS) is an off-line data assimilation system featuring uncoupled land surface model which is driven by observation-based atmospheric forcing. In this study the experiments were conducted with a surface externalized (SURFEX) modelling platform developed at Météo-France. It encompasses the land surface model ISBA-A-gs that simulates photosynthesis and plant growth. The photosynthetic activity depends on the vegetation types. The input soil and vegetation parameters are provided by the ECOCLIMAP II global database which assigns the ecosystem classes in several plant functional types as grassland, crops, deciduous forest and coniferous forest. New versions of the model have been recently developed in order to better describe the agricultural plant functional types. We present a set of observing system simulation experiments (OSSE) which asses leaf area index (LAI) and soil moisture assimilation for improving the land surface estimates in a controlled synthetic environment. Synthetic data were assimilated into ISBA-A-gs using an Extended Kalman Filter (EKF). This allows for an understanding of model responses to an augmentation of the number of crop types and different parameters associated to this modification. In addition, the interactions between uncertainties in the model and in the observations were investigated. This study represents the first step of a process that envisages the extension of LDAS to the new versions of the ISBA-A-gs model in order to assimilate remote sensing observations.

Gravity changes, soil moisture and data assimilation

NASA Astrophysics Data System (ADS)

Walker, J.; Grayson, R.; Rodell, M.; Ellet, K.

2003-04-01

Remote sensing holds promise for near-surface soil moisture and snow mapping, but current techniques do not directly resolve the deeper soil moisture or groundwater. The benefits that would arise from improved monitoring of variations in terrestrial water storage are numerous. The year 2002 saw the launch of NASA's Gravity Recovery And Climate Experiment (GRACE) satellites, which are mapping the Earth's gravity field at such a high level of precision that we expect to be able to infer changes in terrestrial water storage (soil moisture, groundwater, snow, ice, lake, river and vegetation). The project described here has three distinct yet inter-linked components that all leverage off the same ground-based monitoring and land surface modelling framework. These components are: (i) field validation of a relationship between soil moisture and changes in the Earth's gravity field, from ground- and satellite-based measurements of changes in gravity; (ii) development of a modelling framework for the assimilation of gravity data to constrain land surface model predictions of soil moisture content (such a framework enables the downscaling and disaggregation of low spatial (500 km) and temporal (monthly) resolution measurements of gravity change to finer spatial and temporal resolutions); and (iii) further refining the downscaling and disaggregation of space-borne gravity measurements by making use of other remotely sensed information, such as the higher spatial (25 km) and temporal (daily) resolution remotely sensed near-surface soil moisture measurements from the Advanced Microwave Scanning Radiometer (AMSR) instruments on Aqua and ADEOS II. The important field work required by this project will be in the Murrumbidgee Catchment, Australia, where an extensive soil moisture monitoring program by the University of Melbourne is already in place. We will further enhance the current monitoring network by the addition of groundwater wells and additional soil moisture sites. Ground-based gravity measurements will also be made on a monthly basis at each monitoring site. There will be two levels of modelling and monitoring; regional across the entire Murrumbidgee Catchment (100,000 km2), and local across a small sub-catchment (150 km2).

Use of In-Situ and Remotely Sensed Snow Observations for the National Water Model in Both an Analysis and Calibration Framework.

NASA Astrophysics Data System (ADS)

Karsten, L. R.; Gochis, D.; Dugger, A. L.; McCreight, J. L.; Barlage, M. J.; Fall, G. M.; Olheiser, C.

2017-12-01

Since version 1.0 of the National Water Model (NWM) has gone operational in Summer 2016, several upgrades to the model have occurred to improve hydrologic prediction for the continental United States. Version 1.1 of the NWM (Spring 2017) includes upgrades to parameter datasets impacting land surface hydrologic processes. These parameter datasets were upgraded using an automated calibration workflow that utilizes the Dynamic Data Search (DDS) algorithm to adjust parameter values using observed streamflow. As such, these upgrades to parameter values took advantage of various observations collected for snow analysis. In particular, in-situ SNOTEL observations in the Western US, volunteer in-situ observations across the entire US, gamma-derived snow water equivalent (SWE) observations courtesy of the NWS NOAA Corps program, gridded snow depth and SWE products from the Jet Propulsion Laboratory (JPL) Airborne Snow Observatory (ASO), gridded remotely sensed satellite-based snow products (MODIS,AMSR2,VIIRS,ATMS), and gridded SWE from the NWS Snow Data Assimilation System (SNODAS). This study explores the use of these observations to quantify NWM error and improvements from version 1.0 to version 1.1, along with subsequent work since then. In addition, this study explores the use of snow observations for use within the automated calibration workflow. Gridded parameter fields impacting the accumulation and ablation of snow states in the NWM were adjusted and calibrated using gridded remotely sensed snow states, SNODAS products, and in-situ snow observations. This calibration adjustment took place over various ecological regions in snow-dominated parts of the US for a retrospective period of time to capture a variety of climatological conditions. Specifically, the latest calibrated parameters impacting streamflow were held constant and only parameters impacting snow physics were tuned using snow observations and analysis. The adjusted parameter datasets were then used to force the model over an independent period for analysis against both snow and streamflow observations to see if improvements took place. The goal of this work is to further improve snow physics in the NWM, along with identifying areas where further work will take place in the future, such as data assimilation or further forcing improvements.

The MERRA-2 Aerosol Reanalysis, 1980 - onward, Part I: System Description and Data Assimilation Evaluation.

PubMed

Randles, C A; Da Silva, A M; Buchard, V; Colarco, P R; Darmenov, A; Govindaraju, R; Smirnov, A; Holben, B; Ferrare, R; Hair, J; Shinozuka, Y; Flynn, C J

2017-09-01

The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) updates NASA's previous satellite era (1980 - onward) reanalysis system to include additional observations and improvements to the Goddard Earth Observing System, Version 5 (GEOS-5) Earth system model. As a major step towards a full Integrated Earth Systems Analysis (IESA), in addition to meteorological observations, MERRA-2 now includes assimilation of aerosol optical depth (AOD) from various ground- and space-based remote sensing platforms. Here, in the first of a pair of studies, we document the MERRA-2 aerosol assimilation, including a description of the prognostic model (GEOS-5 coupled to the GOCART aerosol module), aerosol emissions, and the quality control of ingested observations. We provide initial validation and evaluation of the analyzed AOD fields using independent observations from ground, aircraft, and shipborne instruments. We demonstrate the positive impact of the AOD assimilation on simulated aerosols by comparing MERRA-2 aerosol fields to an identical control simulation that does not include AOD assimilation. Having shown the AOD evaluation, we take a first look at aerosol-climate interactions by examining the shortwave, clear-sky aerosol direct radiative effect. In our companion paper, we evaluate and validate available MERRA-2 aerosol properties not directly impacted by the AOD assimilation (e.g. aerosol vertical distribution and absorption). Importantly, while highlighting the skill of the MERRA-2 aerosol assimilation products, both studies point out caveats that must be considered when using this new reanalysis product for future studies of aerosols and their interactions with weather and climate.

MODIS Data Assimilation in the CROPGRO model for improving soybean yield estimations

NASA Astrophysics Data System (ADS)

Richetti, J.; Monsivais-Huertero, A.; Ahmad, I.; Judge, J.

2017-12-01

Soybean is one of the main agricultural commodities in the world. Thus, having better estimates of its agricultural production is important. Improving the soybean crop models in Brazil is crucial for better understanding of the soybean market and enhancing decision making, because Brazil is the second largest soybean producer in the world, Parana state is responsible for almost 20% of it, and by itself would be the fourth greatest soybean producer in the world. Data assimilation techniques provide a method to improve spatio-temporal continuity of crops through integration of remotely sensed observations and crop growth models. This study aims to use MODIS EVI to improve DSSAT-CROPGRO soybean yield estimations in the Parana state, southern Brazil. The method uses the Ensemble Kalman filter which assimilates MODIS Terra and Aqua combined products (MOD13Q1 and MYD13Q1) into the CROPGRO model to improve the agricultural production estimates through update of light interception data over time. Expected results will be validated with monitored commercial farms during the period of 2013-2014.

Comparison Between the Use of SAR and Optical Data for Wheat Yield Estimations Using Crop Model Assimilation

NASA Astrophysics Data System (ADS)

Silvestro, Paolo Cosmo; Yang, Hao; Jin, X. L.; Yang, Guijun; Casa, Raffaele; Pignatti, Stefano

2016-08-01

The ultimate aim of this work is to develop methods for the assimilation of the biophysical variables estimated by remote sensing in a suitable crop growth model. Two strategies were followed, one based on the use of Leaf Area Index (LAI) estimated by optical data, and the other based on the use of biomass estimated by SAR. The first one estimates LAI from the reflectance measured by the optical sensors on board of HJ1A, HJ1B and Landsat, using a method based on the training of artificial neural networks (ANN) with PROSAIL model simulations. The retrieved LAI is used to improve wheat yield estimation, using assimilation methods based on the Ensemble Kalman Filter, which assimilate the biophysical variables into growth crop model. The second strategy estimates biomass from SAR imagery. Polarimetric decomposition methods were used based on multi-temporal fully polarimetric Radarsat-2 data during the entire growing season. The estimated biomass was assimilating to FAO Aqua crop model for improving the winter wheat yield estimation, with the Particle Swarm Optimization (PSO) method. These procedures were used in a spatial application with data collected in the rural area of Yangling (Shaanxi Province) in 2014 and were validated for a number of wheat fields for which ground yield data had been recorded and according to statistical yield data for the area.

Snow Radiance Data Assimilation over High Mountain Asia Using the NASA Land Information System and a Well-Trained Support Vector Machine

NASA Astrophysics Data System (ADS)

Kwon, Y.; Forman, B. A.; Yoon, Y.; Kumar, S.

2017-12-01

High Mountain Asia (HMA) has been progressively losing ice and snow in recent decades, which could negatively impact regional water supply and native ecosystems. One goal of this study is to characterize the spatiotemporal variability of snow (and ice) across the HMA region. In addition, modeled snow water equivalent (SWE) estimates will be enhanced through the assimilation of passive microwave brightness temperatures (TB) collected by the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) as part of a radiance assimilation system. The radiance assimilation framework includes the NASA Land Information System (LIS) in conjunction with a well-trained support vector machine (SVM) that acts as the observation operator. The Noah Land Surface Model with multi-parameterization options (Noah-MP) is used as the prior model for simulating snow dynamics. Noah-MP is forced by meteorological fields from the NASA Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) atmospheric reanalysis for the periods 01 Sep. 2002 to 01 Sep. 2011. The radiance assimilation system requires two separate phases: 1) training and 2) assimilation. During the training phase, a nonlinear SVM is generated for three different AMSR-E frequencies - 10.65, 18.7, and 36.5 GHz - at both vertical and horizontal polarization. The trained SVM is then used to predict TB during the assimilation phase. An ensemble Kalman filter will be used to condition the model on AMSR-E brightness temperatures not used during SVM training. The performance of the Noah-MP (with and without radiance assimilation) will be assessed via comparison to in-situ measurements, remotely-sensing geophysical retrievals, and other reanalysis products.

Steady-state chlorophyll fluorescence (Fs) measurements as a tool to follow variations of net CO2 assimilation and stomatal conductance during water-stress in C3 plants.

PubMed

Flexas, Jaume; Escalona, José Mariano; Evain, Sebastian; Gulías, Javier; Moya, Ismaël; Osmond, Charles Barry; Medrano, Hipólito

2002-02-01

Water stress experiments were performed with grapevines (Vitis vinifera L.) and other C3 plants in the field, in potted plants in the laboratory, and with detached leaves. It was found that, in all cases, the ratio of steady state chlorophyll fluorescence (Fs) normalized to dark-adapted intrinsic fluorescence (Fo) inversely correlated with non-photochemical quenching (NPQ). Also, at high irradiance, the ratio Fs/Fo was positively correlated with CO2 assimilation in air, with electron transport rate calculated from fluorescence, and with stomatal conductance, but no clear correlation was observed with qP. The significance of these relationships is discussed. The ratio Fs/Fo, measured with a portable instrument (PAM-2000) or with a remote sensing FIPAM system, provides a good method for the early detection of water stress, and may become a useful guide to irrigation requirements.

The Impact of Ocean Observations in Seasonal Climate Prediction

NASA Technical Reports Server (NTRS)

Rienecker, Michele; Keppenne, Christian; Kovach, Robin; Marshak, Jelena

2010-01-01

The ocean provides the most significant memory for the climate system. Hence, a critical element in climate forecasting with coupled models is the initialization of the ocean with states from an ocean data assimilation system. Remotely-sensed ocean surface fields (e.g., sea surface topography, SST, winds) are now available for extensive periods and have been used to constrain ocean models to provide a record of climate variations. Since the ocean is virtually opaque to electromagnetic radiation, the assimilation of these satellite data is essential to extracting the maximum information content. More recently, the Argo drifters have provided unprecedented sampling of the subsurface temperature and salinity. Although the duration of this observation set has been too short to provide solid statistical evidence of its impact, there are indications that Argo improves the forecast skill of coupled systems. This presentation will address the impact these different observations have had on seasonal climate predictions with the GMAO's coupled model.

The Distribution of Carbon Monoxide in the GOCART Model

NASA Technical Reports Server (NTRS)

Fan, Xiaobiao; Chin, Mian; Einaudi, Franco (Technical Monitor)

2000-01-01

Carbon monoxide (CO) is an important trace gas because it is a significant source of tropospheric Ozone (O3) as well as a major sink for atmospheric hydroxyl radical (OH). The distribution of CO is set by a balance between the emissions, transport, and chemical processes in the atmosphere. The Georgia Tech/Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model is used to simulate the atmospheric distribution of CO. The GOCART model is driven by the assimilated meteorological data from the Goddard Earth Observing System Data Assimilation System (GEOS DAS) in an off-line mode. We study the distribution of CO on three time scales: (1) day to day fluctuation produced by the synoptic waves; (2) seasonal changes due to the annual cycle of CO sources and sinks; and (3) interannual variability induced by dynamics. Comparison of model results with ground based and remote sensing measurements will also be presented.

Impact of SMOS soil moisture data assimilation on NCEP-GFS forecasts

NASA Astrophysics Data System (ADS)

Zhan, X.; Zheng, W.; Meng, J.; Dong, J.; Ek, M.

2012-04-01

Soil moisture is one of the few critical land surface state variables that have long memory to impact the exchanges of water, energy and carbon between the land surface and atmosphere. Accurate information about soil moisture status is thus required for numerical weather, seasonal climate and hydrological forecast as well as for agricultural production forecasts, water management and many other water related economic or social activities. Since the successful launch of ESA's soil moisture ocean salinity (SMOS) mission in November 2009, about 2 years of soil moisture retrievals has been collected. SMOS is believed to be the currently best satellite sensors for soil moisture remote sensing. Therefore, it becomes interesting to examine how the collected SMOS soil moisture data are compared with other satellite-sensed soil moisture retrievals (such as NASA's Advanced Microwave Scanning Radiometer -AMSR-E and EUMETSAT's Advanced Scatterometer - ASCAT)), in situ soil moisture measurements, and how these data sets impact numerical weather prediction models such as the Global Forecast System of NOAA-NCEP. This study implements the Ensemble Kalman filter in GFS to assimilate the AMSR-E, ASCAT and SMOS soil moisture observations after a quantitative assessment of their error rate based on in situ measurements from ground networks around contiguous United States. in situ soil moisture measurements from ground networks (such as USDA Soil Climate Analysis network - SCAN and NOAA's U.S. Climate Reference Network -USCRN) are used to evaluate the GFS soil moisture simulations (analysis). The benefits and uncertainties of assimilating the satellite data products in GFS are examined by comparing the GFS forecasts of surface temperature and rainfall with and without the assimilations. From these examinations, the advantages of SMOS soil moisture data products over other satellite soil moisture data sets will be evaluated. The next step toward operationally assimilating soil moisture and other land observations into GFS will also be discussed.

The potential for geostationary remote sensing of NO2 to improve weather prediction

NASA Astrophysics Data System (ADS)

Liu, X.; Mizzi, A. P.; Anderson, J. L.; Fung, I. Y.; Cohen, R. C.

2017-12-01

Observations of surface winds remain sparse making it challenging to simulate and predict the weather in circumstances of light winds that are most important for poor air quality. Direct measurements of short-lived chemicals from space might be a solution to this challenge. Here we investigate the application of data assimilation of NO­2 columns as will be observed from geostationary orbit to improve predictions and retrospective analysis of surface wind fields. Specifically, synthetic NO2 observations are sampled from a "nature run (NR)" regarded as the true atmosphere. Then NO2 observations are assimilated using EAKF methods into a "control run (CR)" which differs from the NR in the wind field. Wind errors are generated by introducing (1) errors in the initial conditions, (2) creating a model error by using two different formulations for the planetary boundary layer, (3) and by combining both of these effects. Assimilation of NO2 column observations succeeds in reducing wind errors, indicating the prospects for future geostationary atmospheric composition measurements to improve weather forecasting are substantial. We find that due to the temporal heterogeneity of wind errors, the success of this application favors chemical observations of high frequency, such as those from geostationary platform. We also show the potential to improve soil moisture field by assimilating NO­2 columns.

Data assimilation problems in glaciology

NASA Astrophysics Data System (ADS)

Shapero, Daniel

Rising sea levels due to mass loss from Greenland and Antarctica threaten to inundate coastal areas the world over. For the purposes of urban planning and hazard mitigation, policy makers would like to know how much sea-level rise can be anticipated in the next century. To make these predictions, glaciologists use mathematical models of ice sheet flow, together with remotely-sensed observations of the current state of the ice sheets. The quantities that are observable over large spatial scales are the ice surface elevation and speed, and the elevation of the underlying bedrock. There are other quantities, such as the viscosity within the ice and the friction coefficient for sliding over the bed, that are just as important in dictating how fast the glacier flows, but that are not observable at large scales using current methods. These quantities can be inferred from observations by using data assimilation methods, applied to a model of glacier flow. In this dissertation, I will describe my work on data assimilation problems in glaciology. My main contributions so far have been: computing the bed stress underneath the three biggest Greenland outlet glaciers; developing additional tools for glacier modeling and data assimilation in the form of the open-source library icepack ; and improving the statistical methodology through the user of total variation priors.

MUSICA MetOp/IASI {H2O,δD} pair retrieval simulations for validating tropospheric moisture pathways in atmospheric models

NASA Astrophysics Data System (ADS)

Schneider, Matthias; Borger, Christian; Wiegele, Andreas; Hase, Frank; García, Omaira E.; Sepúlveda, Eliezer; Werner, Martin

2017-02-01

The project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) has shown that the sensor IASI aboard the satellite MetOp can measure the free tropospheric {H2O,δD} pair distribution twice per day on a quasi-global scale. Such data are very promising for investigating tropospheric moisture pathways, however, the complex data characteristics compromise their usage in the context of model evaluation studies. Here we present a tool that allows for simulating MUSICA MetOp/IASI {H2O,δD} pair remote sensing data for a given model atmosphere, thereby creating model data that have the remote sensing data characteristics assimilated. This model data can then be compared to the MUSICA data. The retrieval simulation method is based on the physical principles of radiative transfer and we show that the uncertainty of the simulations is within the uncertainty of the MUSICA MetOp/IASI products, i.e. the retrieval simulations are reliable enough. We demonstrate the working principle of the simulator by applying it to ECHAM5-wiso model data. The few case studies clearly reveal the large potential of the MUSICA MetOp/IASI {H2O,δD} data pairs for evaluating modelled moisture pathways. The tool is made freely available in form of MATLAB and Python routines and can be easily connected to any atmospheric water vapour isotopologue model.

Satellite Remote Sensing of Aerosol Forcing

NASA Technical Reports Server (NTRS)

Remer, Lorraine; Kaufman, Yoram; Ramaprasad, Jaya; Procopio, Aline; Levin, Zev

1999-01-01

The role of aerosol forcing remains one of the largest uncertainties in estimating man's impact on the global climate system. One school of thought suggests that remote sensing by satellite sensors will provide the data necessary to narrow these uncertainties. While satellite measurements of direct aerosol forcing appear to be straightforward, satellite measurements of aerosol indirect forcing will be more complicated. Pioneering studies identified indirect aerosol forcing using AVHRR data in the biomass burning regions of Brazil. We have expanded this analysis with AVHRR to include an additional year of data and assimilated water vapor fields. The results show similar latitudinal dependence as reported by Kaufman and Fraser, but by using water vapor observations we conclude that latitude is not a proxy for water vapor and the strength of the indirect effect is not correlated to water vapor amounts. In addition to the AVHRR study we have identified indirect aerosol forcing in Brazil at much smaller spatial scales using the MODIS Airborne Simulator. The strength of the indirect effect appears to be related to cloud type and cloud dynamics. There is a suggestion that some of the cloud dynamics may be influenced by smoke destabilization of the atmospheric column. Finally, this study attempts to quantify remote sensing limitations due to the accuracy limits of the retrieval algorithms. We use a combination of numerical aerosol transport models, ground-based AERONET data and ISCCP cloud climatology to determine how much of the forcing occurs in regions too clean to determine from satellite retrievals.

Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach

NASA Astrophysics Data System (ADS)

Marshall, M.; Tu, K.; Funk, C.; Michaelsen, J.; Williams, P.; Williams, C.; Ardö, J.; Boucher, M.; Cappelaere, B.; de Grandcourt, A.; Nickless, A.; Nouvellon, Y.; Scholes, R.; Kutsch, W.

2013-03-01

Climate change is expected to have the greatest impact on the world's economically poor. In the Sahel, a climatically sensitive region where rain-fed agriculture is the primary livelihood, expected decreases in water supply will increase food insecurity. Studies on climate change and the intensification of the water cycle in sub-Saharan Africa are few. This is due in part to poor calibration of modeled evapotranspiration (ET), a key input in continental-scale hydrologic models. In this study, a remote sensing model of transpiration (the primary component of ET), driven by a time series of vegetation indices, was used to substitute transpiration from the Global Land Data Assimilation System realization of the National Centers for Environmental Prediction, Oregon State University, Air Force, and Hydrology Research Laboratory at National Weather Service Land Surface Model (GNOAH) to improve total ET model estimates for monitoring purposes in sub-Saharan Africa. The performance of the hybrid model was compared against GNOAH ET and the remote sensing method using eight eddy flux towers representing major biomes of sub-Saharan Africa. The greatest improvements in model performance were at humid sites with dense vegetation, while performance at semi-arid sites was poor, but better than the models before hybridization. The reduction in errors using the hybrid model can be attributed to the integration of a simple canopy scheme that depends primarily on low bias surface climate reanalysis data and is driven primarily by a time series of vegetation indices.

Watershed Allied Telemetry Experimental Research

NASA Astrophysics Data System (ADS)

Li, Xin; Li, Xiaowen; Li, Zengyuan; Ma, Mingguo; Wang, Jian; Xiao, Qing; Liu, Qiang; Che, Tao; Chen, Erxue; Yan, Guangjian; Hu, Zeyong; Zhang, Lixin; Chu, Rongzhong; Su, Peixi; Liu, Qinhuo; Liu, Shaomin; Wang, Jindi; Niu, Zheng; Chen, Yan; Jin, Rui; Wang, Weizhen; Ran, Youhua; Xin, Xiaozhou; Ren, Huazhong

2009-11-01

The Watershed Allied Telemetry Experimental Research (WATER) is a simultaneous airborne, satellite-borne, and ground-based remote sensing experiment aiming to improve the observability, understanding, and predictability of hydrological and related ecological processes at a catchment scale. WATER consists of the cold region, forest, and arid region hydrological experiments as well as a hydrometeorology experiment and took place in the Heihe River Basin, a typical inland river basin in the northwest of China. The field campaigns have been completed, with an intensive observation period lasting from 7 March to 12 April, from 15 May to 22 July, and from 23 August to 5 September 2008: in total, 120 days. Twenty-five airborne missions were flown. Airborne sensors including microwave radiometers at L, K, and Ka bands, imaging spectrometer, thermal imager, CCD, and lidar were used. Various satellite data were collected. Ground measurements were carried out at four scales, that is, key experimental area, foci experimental area, experiment site, and elementary sampling plot, using ground-based remote sensing instruments, densified network of automatic meteorological stations, flux towers, and hydrological stations. On the basis of these measurements, the remote sensing retrieval models and algorithms of water cycle variables are to be developed or improved, and a catchment-scale land/hydrological data assimilation system is being developed. This paper reviews the background, scientific objectives, experiment design, filed campaign implementation, and current status of WATER. The analysis of the data will continue over the next 2 years, and limited revisits to the field are anticipated.

Combining Crop Model and Remote Sensing Data at High Resolution for the Assessment of Rice Agricultural Practices in the South-Eastern France (Take 5 Experiment SPOT4-SPOT5)

NASA Astrophysics Data System (ADS)

Courault, D.; Ruget, F.; Talab-ou-Ali, H.; Hagolle, O.; Delmotte, S.; Barbier, J. M.; Boschetti, M.; Mouret, J. C.

2016-08-01

Crop systems are constantly changing due to modifications in the agricultural practices to respond to market changes, the constraints of the environment, the climate hazards... Rice cultivation practiced in the Camargue region (SE France) have decreased these last years, however rice plays a crucial role for the hydrological balance of the region and for crop systems desalinizing soils. The aim of this study is to analyze the potentialities of remote sensing data acquired at high spatial and temporal resolution (HRST) to identify the main agricultural practices and estimate their impact on rice production. A large dataset acquired over the Camargue from the Take5 experiment (SPOT4 in 2013 and SPOT5 in 2015), completed by Landsat data has been used. Two assimilation methods of HRST data were evaluated within a crop model. Results showed the impact of the spatial variability of practices on the yields. The sowing dates were retrieved from inverse procedures and gave satisfactory results compared to ground surveys.

A simple nudging scheme to assimilate ASCAT soil moisture data in the WRF model

NASA Astrophysics Data System (ADS)

Capecchi, V.; Gozzini, B.

2012-04-01

The present work shows results obtained in a numerical experiment using the WRF (Weather and Research Forecasting, www.wrf-model.org) model. A control run where soil moisture is constrained by GFS global analysis is compared with a test run where soil moisture analysis is obtained via a simple nudging scheme using ASCAT data. The basic idea of the assimilation scheme is to "nudge" the first level (0-10 cm below ground in NOAH model) of volumetric soil moisture of the first-guess (say θ(b,1) derived from global model) towards the ASCAT derived value (say ^θ A). The soil moisture analysis θ(a,1) is given by: { θ + K (^θA - θ ) l = 1 θ(a,1) = θ(b,l) (b,l) l > 1 (b,l) (1) where l is the model soil level. K is a constant scalar value that is user specified and in this study it is equal to 0.2 (same value as in similar studies). Soil moisture is critical for estimating latent and sensible heat fluxes as well as boundary layer structure. This parameter is, however, poorly assimilated in current global and regional numerical models since no extensive soil moisture observation network exists. Remote sensing technologies offer a synoptic view of the dynamics and spatial distribution of soil moisture with a frequent temporal coverage and with a horizontal resolution similar to mesoscale NWP model. Several studies have shown that measurements of normalized backscatter (surface soil wetness) from the Advanced Scatterometer (ASCAT) operating at microwave frequencies and boarded on the meteorological operational (Metop) satellite, offer quality information about surface soil moisture. Recently several studies deal with the implementation of simple assimilation procedures (nudging, Extended Kalman Filter, etc...) to integrate ASCAT data in NWP models. They found improvements in screen temperature predictions, particularly in areas such as North-America and in the Tropics, where it is strong the land-atmosphere coupling. The ECMWF (Newsletter No. 127) is currently implementing and testing an EKF for combining conventional observations and remote sensed soil moisture data in order to produce a more accurate analysis. In the present work verification skills (RMSE, BIAS, correlation) of both control and test run are presented using observed data collected by International Soil Moisture Network. Moreover improvements in temperature predictions are evaluated.

USC/JPL GAIM: A Real-Time Global Ionospheric Data Assimilation Model

NASA Astrophysics Data System (ADS)

Mandrake, L.; Wilson, B. D.; Hajj, G.; Wang, C.; Pi, X. `; Iijima, B.

2004-12-01

We are in the midst of a revolution in ionospheric remote sensing driven by the illuminating powers of ground and space-based GPS receivers, new UV remote sensing satellites, and the advent of data assimilation techniques for space weather. The University of Southern Califronia (USC) and the Jet Propulsion Laboratory (JPL) have jointly developed a Global Assimilative Ionospheric Model (GAIM) to monitor space weather, study storm effects, and provide ionospheric calibration for DoD customers and NASA flight projects. GAIM is a physics-based 3D data assimilation model that uses both 4DVAR and Kalman filter techniques to solve for the ion & electron density state and key drivers such as equatorial electrodynamics, neutral winds, and production terms. GAIM accepts as input ground GPS TEC data from 900+ sites, occultation links from CHAMP, SAC-C, IOX, and the coming COSMIC constellation, UV limb and nadir scans from the TIMED and DMSP satellites, and in situ data from a variety of satellites (C/NOFS & DMSP). GAIM can ingest multiple data sources in real time, updates the 3D electron density grid every 5 minutes, and solves for improved drivers every 1-2 hours. GAIM density retrievals have been validated by comparisons to vertical TEC measurements from TOPEX & JASON, slant TEC measurements from independent GPS sites, density profiles from ionosondes & incoherent scatter radars, and alternative tomographic retrievals. Daily USC/JPL GAIM runs have been operational since March 2003 using 100-200 ground GPS sites as input and TOPEX/JASON and ionosondes for daily validation. A prototype real-time GAIM system has been running since May 2004. RT GAIM ingests TEC data from 77+ streaming GPS sites every 5 minutes, adds more TEC for better global coverage every hour from hourly GPS sites, and updates the ionospheric state every 5 minutes using the Kalman filter. We plan to add TEC links from COSMIC occultations and UV radiance data from the DMSP satellites, when they become available, to the daily and RT GAIM runs. Our presentation will include results from numerous validation case studies and one year of JASON validation statistics. Customers are currently evaluating the accuracy of USC/JPL GAIM "nowcasts" for ray tracing applications and trans-ionospheric path delay calibration.

An Improved Analysis of Forest Carbon Dynamics using Data Assimilation

NASA Technical Reports Server (NTRS)

Williams, Mathew; Schwarz, Paul A.; Law, Beverly E.; Kurpius, Meredith R.

2005-01-01

There are two broad approaches to quantifying landscape C dynamics - by measuring changes in C stocks over time, or by measuring fluxes of C directly. However, these data may be patchy, and have gaps or biases. An alternative approach to generating C budgets has been to use process-based models, constructed to simulate the key processes involved in C exchange. However, the process of model building is arguably subjective, and parameters may be poorly defined. This paper demonstrates why data assimilation (DA) techniques - which combine stock and flux observations with a dynamic model - improve estimates of, and provide insights into, ecosystem carbon (C) exchanges. We use an ensemble Kalman filter (EnKF) to link a series of measurements with a simple box model of C transformations. Measurements were collected at a young ponderosa pine stand in central Oregon over a 3-year period, and include eddy flux and soil C02 efflux data, litterfall collections, stem surveys, root and soil cores, and leaf area index data. The simple C model is a mass balance model with nine unknown parameters, tracking changes in C storage among five pools; foliar, wood and fine root pools in vegetation, and also fresh litter and soil organic matter (SOM) plus coarse woody debris pools. We nested the EnKF within an optimization routine to generate estimates from the data of the unknown parameters and the five initial conditions for the pools. The efficacy of the DA process can be judged by comparing the probability distributions of estimates produced with the EnKF analysis vs. those produced with reduced data or model alone. Using the model alone, estimated net ecosystem exchange of C (NEE)= -251 f 197g Cm-2 over the 3 years, compared with an estimate of -419 f 29gCm-2 when all observations were assimilated into the model. The uncertainty on daily measurements of NEE via eddy fluxes was estimated at 0.5gCm-2 day-1, but the uncertainty on assimilated estimates averaged 0.47 g Cm-2 day-1, and only exceeded 0.5gC m-2 day-1 on days where neither eddy flux nor soil efflux data were available. In generating C budgets, the assimilation process reduced the uncertainties associated with using data or model alone and the forecasts of NEE were statistically unbiased estimates. The results of the analysis emphasize the importance of time series as constraints. Occasional, rare measurements of stocks have limited use in constraining the estimates of other components of the C cycle. Long time series are particularly crucial for improving the analysis of pools with long time constants, such as SOM, woody biomass, and woody debris. Long-running forest stem surveys, and tree ring data, offer a rich resource that could be assimilated to provide an important constraint on C cycling of slow pools. For extending estimates of NEE across regions, DA can play a further important role, by assimilating remote-sensing data into the analysis of C cycles. We show, via sensitivity analysis, how assimilating an estimate of photosynthesis - which might be provided indirectly by remotely sensed data - improves the analysis of NEE.

The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description and Data Assimilation Evaluation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Randles, C. A.; da Silva, A. M.; Buchard, V.

The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) updates NASA’s previous satellite era (1980 – onward) reanalysis system to include additional observations and improvements to the Goddard Earth Observing System, Version 5 (GEOS-5) Earth system model. As a major step towards a full Integrated Earth Systems Analysis (IESA), in addition to meteorological observations, MERRA-2 now includes assimila-tion of aerosol optical depth (AOD) from various ground- and space-based remote sensing platforms. Here, in the first of a pair of studies, we document the MERRA-2 aerosol assimilation, including a description of the prognostic model (GEOS-5 coupled to the GOCARTmore » aerosol module), aerosol emissions, and the quality control of ingested observations. We provide initial validation and evaluation of the analyzed AOD fields using independent observations rom ground, aircraft, and shipborne instruments. We demonstrate the pos-itive impact of the AOD assimilation on simulated aerosols by comparing MERRA-2 aerosol fields to an identical control simulation that does not in-clude AOD assimilation. Having shown the AOD evaluation, we take a first look at aerosol-climate interactions by examining the shortwave, clear-sky aerosol direct radiative effect. In our companion paper, we evaluate and validate available MERRA-2 aerosol properties not directly impacted by the AOD assimilation (e.g. aerosol vertical distribution and absorption). Importantly, while highlighting the skill of the MERRA-2 aerosol assimilation products, both studies point out caveats that must be considered when using this new reanalysis product for future studies of aerosols and their interactions with weather and climate.« less

Coral Reef Color: Remote and In-Situ Imaging Spectroscopy of Reef Structure and Function

NASA Astrophysics Data System (ADS)

Hochberg, E. J.

2016-02-01

Coral reefs are threatened at local to global scales by a litany of anthropogenic impacts, including overfishing, coastal development, marine and watershed pollution, rising ocean temperatures, and ocean acidification. However, available data for the primary indicator of coral reef condition — proportional cover of living coral — are surprisingly sparse and show patterns that contradict the prevailing understanding of how environment impacts reef condition. Remote sensing is the only available tool for acquiring synoptic, uniform data on reef condition at regional to global scales. Discrimination between coral and other reef benthos relies on narrow wavebands afforded by imaging spectroscopy. The same spectral information allows non-invasive quantification of photosynthetic pigment composition, which shows unexpected phenological trends. There is also potential to link biodiversity with optical diversity, though there has been no effort in that direction. Imaging spectroscopy underlies the light-use efficiency model for reef primary production by quantifying light capture, which in turn indicates biochemical capacity for CO2 assimilation. Reef calcification is strongly correlated with primary production, suggesting the possibility for an optics-based model of that aspect of reef function, as well. By scaling these spectral models for use with remote sensing, we can vastly improve our understanding of reef structure, function, and overall condition across regional to global scales. By analyzing those remote sensing products against ancillary environmental data, we can construct secondary models to predict reef futures in the era of global change. This final point is the objective of CORAL (COral Reef Airborne Laboratory), a three-year project funded under NASA's Earth Venture Suborbital-2 program to investigate the relationship between coral reef condition at the ecosystem scale and various nominal biogeophysical forcing parameters.

Coral Reef Color: Remote and In-Situ Imaging Spectroscopy of Reef Structure and Function

NASA Astrophysics Data System (ADS)

Hochberg, E. J.

2015-12-01

Coral reefs are threatened at local to global scales by a litany of anthropogenic impacts, including overfishing, coastal development, marine and watershed pollution, rising ocean temperatures, and ocean acidification. However, available data for the primary indicator of coral reef condition — proportional cover of living coral — are surprisingly sparse and show patterns that contradict the prevailing understanding of how environment impacts reef condition. Remote sensing is the only available tool for acquiring synoptic, uniform data on reef condition at regional to global scales. Discrimination between coral and other reef benthos relies on narrow wavebands afforded by imaging spectroscopy. The same spectral information allows non-invasive quantification of photosynthetic pigment composition, which shows unexpected phenological trends. There is also potential to link biodiversity with optical diversity, though there has been no effort in that direction. Imaging spectroscopy underlies the light-use efficiency model for reef primary production by quantifying light capture, which in turn indicates biochemical capacity for CO2 assimilation. Reef calcification is strongly correlated with primary production, suggesting the possibility for an optics-based model of that aspect of reef function, as well. By scaling these spectral models for use with remote sensing, we can vastly improve our understanding of reef structure, function, and overall condition across regional to global scales. By analyzing those remote sensing products against ancillary environmental data, we can construct secondary models to predict reef futures in the era of global change. This final point is the objective of CORAL (COral Reef Airborne Laboratory), a three-year project funded under NASA's Earth Venture Suborbital-2 program to investigate the relationship between coral reef condition at the ecosystem scale and various nominal biogeophysical forcing parameters.

Development of a software framework for data assimilation and its applications for streamflow forecasting in Japan

NASA Astrophysics Data System (ADS)

Noh, S. J.; Tachikawa, Y.; Shiiba, M.; Yorozu, K.; Kim, S.

2012-04-01

Data assimilation methods have received increased attention to accomplish uncertainty assessment and enhancement of forecasting capability in various areas. Despite of their potentials, applicable software frameworks to probabilistic approaches and data assimilation are still limited because the most of hydrologic modeling software are based on a deterministic approach. In this study, we developed a hydrological modeling framework for sequential data assimilation, so called MPI-OHyMoS. MPI-OHyMoS allows user to develop his/her own element models and to easily build a total simulation system model for hydrological simulations. Unlike process-based modeling framework, this software framework benefits from its object-oriented feature to flexibly represent hydrological processes without any change of the main library. Sequential data assimilation based on the particle filters is available for any hydrologic models based on MPI-OHyMoS considering various sources of uncertainty originated from input forcing, parameters and observations. The particle filters are a Bayesian learning process in which the propagation of all uncertainties is carried out by a suitable selection of randomly generated particles without any assumptions about the nature of the distributions. In MPI-OHyMoS, ensemble simulations are parallelized, which can take advantage of high performance computing (HPC) system. We applied this software framework for short-term streamflow forecasting of several catchments in Japan using a distributed hydrologic model. Uncertainty of model parameters and remotely-sensed rainfall data such as X-band or C-band radar is estimated and mitigated in the sequential data assimilation.

Assimilation of SMOS Retrieved Soil Moisture into the Land Information System

NASA Technical Reports Server (NTRS)

Blankenship, Clay B.; Case, Jonathan L.; Zavodsky, Bradley T.

2014-01-01

Soil moisture is a crucial variable for weather prediction because of its influence on evaporation and surface heat fluxes. It is also of critical importance for drought and flood monitoring and prediction and for public health applications such as monitoring vector-borne diseases. Land surface modeling benefits greatly from regular updates with soil moisture observations via data assimilation. Satellite remote sensing is the only practical observation type for this purpose in most areas due to its worldwide coverage. The newest operational satellite sensor for soil moisture is the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) instrument aboard the Soil Moisture and Ocean Salinity (SMOS) satellite. The NASA Short-term Prediction Research and Transition Center (SPoRT) has implemented the assimilation of SMOS soil moisture observations into the NASA Land Information System (LIS), an integrated modeling and data assimilation software platform. We present results from assimilating SMOS observations into the Noah 3.2 land surface model within LIS. The SMOS MIRAS is an L-band radiometer launched by the European Space Agency in 2009, from which we assimilate Level 2 retrievals [1] into LIS-Noah. The measurements are sensitive to soil moisture concentration in roughly the top 2.5 cm of soil. The retrievals have a target volumetric accuracy of 4% at a resolution of 35-50 km. Sensitivity is reduced where precipitation, snowcover, frozen soil, or dense vegetation is present. Due to the satellite's polar orbit, the instrument achieves global coverage twice daily at most mid- and low-latitude locations, with only small gaps between swaths.

Coupling the Community Atmospheric Model (CAM) with the Statistical Spectral Interpolation (SSI) System under ESMF

NASA Technical Reports Server (NTRS)

daSilva, Arlindo

2004-01-01

The first set of interoperability experiments illustrates the role ESMF can play in integrating the national Earth science resources. Using existing data assimilation technology from NCEP and the National Weather Service, the Community Atmosphere Model (CAM) was able to ingest conventional and remotely sensed observations, a capability that could open the door to using CAM for weather as well as climate prediction. CAM, which includes land surface capabilities, was developed by NCAR, with key components from GSFC. In this talk we will describe the steps necessary for achieving the coupling of these two systems.

On the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing data

NASA Astrophysics Data System (ADS)

Zhou, Lu; Xu, Shiming; Liu, Jiping; Wang, Bin

2018-03-01

The accurate knowledge of sea ice parameters, including sea ice thickness and snow depth over the sea ice cover, is key to both climate studies and data assimilation in operational forecasts. Large-scale active and passive remote sensing is the basis for the estimation of these parameters. In traditional altimetry or the retrieval of snow depth with passive microwave remote sensing, although the sea ice thickness and the snow depth are closely related, the retrieval of one parameter is usually carried out under assumptions over the other. For example, climatological snow depth data or as derived from reanalyses contain large or unconstrained uncertainty, which result in large uncertainty in the derived sea ice thickness and volume. In this study, we explore the potential of combined retrieval of both sea ice thickness and snow depth using the concurrent active altimetry and passive microwave remote sensing of the sea ice cover. Specifically, laser altimetry and L-band passive remote sensing data are combined using two forward models: the L-band radiation model and the isostatic relationship based on buoyancy model. Since the laser altimetry usually features much higher spatial resolution than L-band data from the Soil Moisture Ocean Salinity (SMOS) satellite, there is potentially covariability between the observed snow freeboard by altimetry and the retrieval target of snow depth on the spatial scale of altimetry samples. Statistically significant correlation is discovered based on high-resolution observations from Operation IceBridge (OIB), and with a nonlinear fitting the covariability is incorporated in the retrieval algorithm. By using fitting parameters derived from large-scale surveys, the retrievability is greatly improved compared with the retrieval that assumes flat snow cover (i.e., no covariability). Verifications with OIB data show good match between the observed and the retrieved parameters, including both sea ice thickness and snow depth. With detailed analysis, we show that the error of the retrieval mainly arises from the difference between the modeled and the observed (SMOS) L-band brightness temperature (TB). The narrow swath and the limited coverage of the sea ice cover by altimetry is the potential source of error associated with the modeling of L-band TB and retrieval. The proposed retrieval methodology can be applied to the basin-scale retrieval of sea ice thickness and snow depth, using concurrent passive remote sensing and active laser altimetry based on satellites such as ICESat-2 and WCOM.

Using Large-Scale Precipitation to Validate AMSR-E Satellite Soil Moisture Estimates by Means of Mutual Information

NASA Astrophysics Data System (ADS)

Tuttle, S. E.; Salvucci, G.

2013-12-01

Validation of remotely sensed soil moisture is complicated by the difference in scale between remote sensing footprints and traditional ground-based soil moisture measurements. To address this issue, a new method was developed to evaluate the useful information content of remotely sensed soil moisture data using only large-scale precipitation (i.e. without modeling). Under statistically stationary conditions [Salvucci, 2001], precipitation conditionally averaged according to soil moisture (denoted E[P|S]) results in a sigmoidal shape in a manner that reflects the dependence of drainage, runoff, and evapotranspiration on soil moisture. However, errors in satellite measurement and algorithmic conversion of satellite data to soil moisture can degrade this relationship. Thus, remotely sensed soil moisture products can be assessed by the degree to which the natural sigmoidal relationship is preserved. The metric of mutual information was used as an error-dependent measure of the strength of the sigmoidal relationship, calculated from a two-dimensional histogram of soil moisture versus precipitation estimated using Gaussian mixture models. Three AMSR-E algorithms (VUA-NASA [Owe et al., 2001], NASA [Njoku et al., 2003], and U. Montana [Jones & Kimball, 2010]) were evaluated with the method for a nine-year period (2002-2011) over the contiguous United States at ¼° latitude-longitude resolution, using precipitation from the North American Land Data Assimilation System (NLDAS). The U. Montana product resulted in the highest mutual information for 57% of the region, followed by VUA-NASA and NASA at 40% and 3%, respectively. Areas where the U. Montana product yielded the maximum mutual information generally coincided with low vegetation biomass and flatter terrain, while the VUA-NASA product contained more useful information in more rugged and highly vegetated areas. Additionally, E[P|S] curves resulting from the Gaussian mixture method can potentially be decomposed into their conditional evapotranspiration and drainage plus runoff components using matrix factorization methods, allowing for time-averaged mapping of these fluxes over the study area.

Improving Water Balance Estimation in the Nile by Combining Remote Sensing and Hydrological Modelling: a Template for Ungauged Basins

NASA Astrophysics Data System (ADS)

Gleason, C. J.; Wada, Y.; Wang, J.

2017-12-01

Declining gauging infrastructure and fractious water politics have decreased available information about river flows globally, especially in international river basins. Remote sensing and water balance modelling are frequently cited as a potential solutions, but these techniques largely rely on the same in decline gauge data to constrain or parameterize discharge estimates, thus creating a circular approach to estimating discharge inapplicable to ungauged basins. To address this, we here combine a discontinued gauge, remotely sensed discharge estimates made via at-many-stations hydraulic geometry (AMHG) and Landsat data, and the PCR-GLOBWB hydrological model to estimate discharge for an ungauged time period for the Lower Nile (1978-present). Specifically, we first estimate initial discharges from 86 Landsat images and AMHG (1984-2015), and then use these flow estimates to tune the hydrologic model. Our tuning methodology is purposefully simple and can be easily applied to any model without the need for calibration/parameterization. The resulting tuned modelled hydrograph shows large improvement in flow magnitude over previous modelled hydrographs, and validation of tuned monthly model output flows against the historical gauge yields an RMSE of 343 m3/s (33.7%). By contrast, the original simulation had an order-of-magnitude flow error. This improvement is substantial but not perfect: modelled flows have a one-to two-month wet season lag and a negative bias. More sophisticated model calibration and training (e.g. data assimilation) is needed to improve upon our results, however, our results achieved by coupling physical models and remote sensing is a promising first step and proof of concept toward future modelling of ungauged flows. This is especially true as massive cloud computing via Google Earth Engine makes our method easily applicable to any basin without current gauges. Finally, we purposefully do not offer prescriptive solutions for Nile management, and rather hope that the methods demonstrated herein can prove useful to river stakeholders in managing their own water.

Continuous data assimilation for downscaling large-footprint soil moisture retrievals

NASA Astrophysics Data System (ADS)

Altaf, Muhammad U.; Jana, Raghavendra B.; Hoteit, Ibrahim; McCabe, Matthew F.

2016-10-01

Soil moisture is a key component of the hydrologic cycle, influencing processes leading to runoff generation, infiltration and groundwater recharge, evaporation and transpiration. Generally, the measurement scale for soil moisture is found to be different from the modeling scales for these processes. Reducing this mismatch between observation and model scales in necessary for improved hydrological modeling. An innovative approach to downscaling coarse resolution soil moisture data by combining continuous data assimilation and physically based modeling is presented. In this approach, we exploit the features of Continuous Data Assimilation (CDA) which was initially designed for general dissipative dynamical systems and later tested numerically on the incompressible Navier-Stokes equation, and the Benard equation. A nudging term, estimated as the misfit between interpolants of the assimilated coarse grid measurements and the fine grid model solution, is added to the model equations to constrain the model's large scale variability by available measurements. Soil moisture fields generated at a fine resolution by a physically-based vadose zone model (HYDRUS) are subjected to data assimilation conditioned upon coarse resolution observations. This enables nudging of the model outputs towards values that honor the coarse resolution dynamics while still being generated at the fine scale. Results show that the approach is feasible to generate fine scale soil moisture fields across large extents, based on coarse scale observations. Application of this approach is likely in generating fine and intermediate resolution soil moisture fields conditioned on the radiometerbased, coarse resolution products from remote sensing satellites.

A data assimilation system combining CryoSat-2 data and hydrodynamic river models

NASA Astrophysics Data System (ADS)

Schneider, Raphael; Ridler, Marc-Etienne; Godiksen, Peter Nygaard; Madsen, Henrik; Bauer-Gottwein, Peter

2018-02-01

There are numerous hydrologic studies using satellite altimetry data from repeat-orbit missions such as Envisat or Jason over rivers. This study is one of the first examples for the combination of altimetry from drifting-ground track satellite missions, namely CryoSat-2, with a river model. CryoSat-2 SARIn Level 2 data is used to improve a 1D hydrodynamic model of the Brahmaputra River in South Asia, which is based on the Saint-Venant equations for unsteady flow and set up in the MIKE HYDRO River software. After calibration of discharge and water level the hydrodynamic model can accurately and bias-free represent the spatio-temporal variations of water levels. A data assimilation framework has been developed and linked with the model. It is a flexible framework that can assimilate water level data which are arbitrarily distributed in time and space. The setup has been used to assimilate CryoSat-2 water level observations over the Assam valley for the years 2010-2015, using an Ensemble Transform Kalman Filter (ETKF). Performance improvement in terms of discharge forecasting skill was then evaluated. For experiments with synthetic CryoSat-2 data the continuous ranked probability score (CRPS) was improved by up to 32%, whilst for experiments assimilating real data it could be improved by up to 10%. The developed methods are expected to be transferable to other rivers and altimeter missions. The model setup and calibration is based almost entirely on globally available remote sensing data.

River discharge estimation from synthetic SWOT-type observations using variational data assimilation and the full Saint-Venant hydraulic model

NASA Astrophysics Data System (ADS)

Oubanas, Hind; Gejadze, Igor; Malaterre, Pierre-Olivier; Mercier, Franck

2018-04-01

The upcoming Surface Water and Ocean Topography satellite mission, to be launched in 2021, will measure river water surface elevation, slope and width, with an unprecedented level of accuracy for a remote sensing tool. This work investigates the river discharge estimation from synthetic SWOT observations, in the presence of strong uncertainties in the model inputs, i.e. the river bathymetry and bed roughness. The estimation problem is solved by a novel variant of the standard variational data assimilation, the '4D-Var' method, involving the full Saint-Venant 1.5D-network hydraulic model SIC2. The assimilation scheme simultaneously estimates the discharge, bed elevation and bed roughness coefficient and is designed to assimilate both satellite and in situ measurements. The method is tested on a 50 km-long reach of the Garonne River during a five-month period of the year 2010, characterized by multiple flooding events. First, the impact of the sampling frequency on discharge estimation is investigated. Secondly, discharge as well as the spatially distributed bed elevation and bed roughness coefficient are determined simultaneously. Results demonstrate feasibility and efficiency of the chosen combination of the estimation method and of the hydraulic model. Assimilation of the SWOT data results into an accurate estimation of the discharge at observation times, and a local improvement in the bed level and bed roughness coefficient. However, the latter estimates are not generally usable for different independent experiments.

Examining the Suitability of a Sparse In Situ Soil Moisture Monitoring Network for Assimilation into a Spatially Distributed Hydrologic Model

NASA Astrophysics Data System (ADS)

De Vleeschouwer, N.; Verhoest, N.; Pauwels, V. R. N.

2015-12-01

The continuous monitoring of soil moisture in a permanent network can yield an interesting data product for use in hydrological data assimilation. Major advantages of in situ observations compared to remote sensing products are the potential vertical extent of the measurements, the finer temporal resolution of the observation time series, the smaller impact of land cover variability on the observation bias, etc. However, two major disadvantages are the typical small integration volume of in situ measurements and the often large spacing between monitoring locations. This causes only a small part of the modelling domain to be directly observed. Furthermore, the spatial configuration of the monitoring network is typically temporally non-dynamic. Therefore two questions can be raised. Do spatially sparse in situ soil moisture observations contain a sufficient data representativeness to successfully assimilate them into the largely unobserved spatial extent of a distributed hydrological model? And if so, how is this assimilation best performed? Consequently two important factors that can influence the success of assimilating in situ monitored soil moisture are the spatial configuration of the monitoring network and the applied assimilation algorithm. In this research the influence of those factors is examined by means of synthetic data-assimilation experiments. The study area is the ± 100 km² catchment of the Bellebeek in Flanders, Belgium. The influence of the spatial configuration is examined by varying the amount of locations and their position in the landscape. The latter is performed using several techniques including temporal stability analysis and clustering. Furthermore the observation depth is considered by comparing assimilation of surface layer (5 cm) and deeper layer (50 cm) observations. The impact of the assimilation algorithm is assessed by comparing the performance obtained with two well-known algorithms: Newtonian nudging and the Ensemble Kalman Filter.

Assimilation of ASCAT near-surface soil moisture into the SIM hydrological model over France

NASA Astrophysics Data System (ADS)

Draper, C.; Mahfouf, J.-F.; Calvet, J.-C.; Martin, E.; Wagner, W.

2011-12-01

This study examines whether the assimilation of remotely sensed near-surface soil moisture observations might benefit an operational hydrological model, specifically Météo-France's SAFRAN-ISBA-MODCOU (SIM) model. Soil moisture data derived from ASCAT backscatter observations are assimilated into SIM using a Simplified Extended Kalman Filter (SEKF) over 3.5 years. The benefit of the assimilation is tested by comparison to a delayed cut-off version of SIM, in which the land surface is forced with more accurate atmospheric analyses, due to the availability of additional atmospheric observations after the near-real time data cut-off. However, comparing the near-real time and delayed cut-off SIM models revealed that the main difference between them is a dry bias in the near-real time precipitation forcing, which resulted in a dry bias in the root-zone soil moisture and associated surface moisture flux forecasts. While assimilating the ASCAT data did reduce the root-zone soil moisture dry bias (by nearly 50%), this was more likely due to a bias within the SEKF, than due to the assimilation having accurately responded to the precipitation errors. Several improvements to the assimilation are identified to address this, and a bias-aware strategy is suggested for explicitly correcting the model bias. However, in this experiment the moisture added by the SEKF was quickly lost from the model surface due to the enhanced surface fluxes (particularly drainage) induced by the wetter soil moisture states. Consequently, by the end of each winter, during which frozen conditions prevent the ASCAT data from being assimilated, the model land surface had returned to its original (dry-biased) climate. This highlights that it would be more effective to address the precipitation bias directly, than to correct it by constraining the model soil moisture through data assimilation.

The Path from Large Earth Science Datasets to Information

NASA Astrophysics Data System (ADS)

Vicente, G. A.

2013-12-01

The NASA Goddard Earth Sciences Data (GES) and Information Services Center (DISC) is one of the major Science Mission Directorate (SMD) for archiving and distribution of Earth Science remote sensing data, products and services. This virtual portal provides convenient access to Atmospheric Composition and Dynamics, Hydrology, Precipitation, Ozone, and model derived datasets (generated by GSFC's Global Modeling and Assimilation Office), the North American Land Data Assimilation System (NLDAS) and the Global Land Data Assimilation System (GLDAS) data products (both generated by GSFC's Hydrological Sciences Branch). This presentation demonstrates various tools and computational technologies developed in the GES DISC to manage the huge volume of data and products acquired from various missions and programs over the years. It explores approaches to archive, document, distribute, access and analyze Earth Science data and information as well as addresses the technical and scientific issues, governance and user support problem faced by scientists in need of multi-disciplinary datasets. It also discusses data and product metrics, user distribution profiles and lessons learned through interactions with the science communities around the world. Finally it demonstrates some of the most used data and product visualization and analyses tools developed and maintained by the GES DISC.

Setting Up a Sentinel 1 Based Soil Moisture - Data Assimilation System for Flash Flood Risk Mitigation

NASA Astrophysics Data System (ADS)

Cenci, Luca; Pulvirenti, Luca; Boni, Giorgio; Chini, Marco; Matgen, Patrick; Gabellani, Simone; Squicciarino, Giuseppe; Pierdicca, Nazzareno

2017-04-01

Several studies have shown that the assimilation of satellite-derived soil moisture products (SM-DA) within hydrological modelling is able to reduce the uncertainty of discharge predictions. This can be exploited for improving early warning systems (EWS) and it is thus particularly useful for flash flood risk mitigation (Cenci et al., 2016a). The objective of this research was to evaluate the potentialities of an advanced SM-DA system based on the assimilation of synthetic aperture radar (SAR) observations derived from Sentinel 1 (S1) acquisitions. A time-continuous, spatially-distributed, physically-based hydrological model was used: Continuum (Silvestro et al., 2013). The latter is currently exploited for civil protection activities in Italy, both at national and at regional scale. Therefore, its adoption allows for a better understanding of the real potentialities of the aforementioned SM-DA system for improving EWS. The novelty of this research consisted in the use of S1-derived SM products obtained by using a multitemporal retrieval algorithm (Cenci et al., 2016b) in which the correction of the vegetation effect was obtained by means of both SAR (Cosmo-SkyMed) and optical (Landsat) images. The maps were characterised by a comparatively higher spatial/lower temporal resolution (respectively, 100 m and 12 days) w.r.t. maps obtained from commonly used microwave sensors for such applications (e.g. the Advanced SCATterometer, ASCAT). The experiment was carried out in the period October 2014 - February 2015 in an exemplifying Mediterranean catchment prone to flash floods: the Orba Basin (Italy). The Nudging assimilation scheme was chosen for its computational efficiency, particularly useful for operational applications. The impact of the assimilation was evaluated by comparing simulated and observed discharge values. In particular, it was analysed the impact of the assimilation on higher flows. Results were compared with those obtained by assimilating an ASCAT-derived SM product (H08) that can be considered at high spatial resolution (1 km) for hydrological applications and high temporal resolution (36 h) (Wagner et al., 2013). Findings revealed the potentialities of a S1-based SM-DA system for improving discharge predictions, especially of higher flows, and suggested the more appropriate pre-processing techniques to apply to S1 data before the assimilation. The comparison with H08 highlighted the importance of the temporal resolution of the observations. Results are promising but further research is needed before the actual implementation of the aforementioned S1-based SM-DA system for operational applications. References - Cenci L., et al.: Assimilation of H-SAF Soil Moisture Products for Flash Flood Early Warning Systems. Case Study: Mediterranean Catchments, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.}, 9(12), 5634-5646, doi:10.1109/JSTARS.2016.2598475, 2016a. - Cenci L., et al.: Satellite Soil Moisture Assimilation: Preliminary Assessment of the Sentinel 1 Potentialities, 2016 IEEE Int. Geosci. Remote Sens. Symp. (IGARSS), Beijing, 3098-3101, doi:10.1109/IGARSS.2016.7729801, 2016b. - Silvestro F., et al.: Exploiting Remote Sensing Land Surface Temperature in Distributed Hydrological Modelling: the Example of the Continuum Model, Hydrol. Earth Syst. Sci., 17(1), 39-62, doi:10.5194/hess-17-39-2013, 2013. - Wagner W., et al.: The ASCAT Soil Moisture Product: A Review of its Specifications, Validation Results, and Emerging Applications, Meteorol. Zeitschrift, 22(1), 5-33, doi:10.1127/0941-2948/2013/0399, 2013.

Application of data assimilation methods for analysis and integration of observed and modeled Arctic Sea ice motions

NASA Astrophysics Data System (ADS)

Meier, Walter Neil

This thesis demonstrates the applicability of data assimilation methods to improve observed and modeled ice motion fields and to demonstrate the effects of assimilated motion on Arctic processes important to the global climate and of practical concern to human activities. Ice motions derived from 85 GHz and 37 GHz SSM/I imagery and estimated from two-dimensional dynamic-thermodynamic sea ice models are compared to buoy observations. Mean error, error standard deviation, and correlation with buoys are computed for the model domain. SSM/I motions generally have a lower bias, but higher error standard deviations and lower correlation with buoys than model motions. There are notable variations in the statistics depending on the region of the Arctic, season, and ice characteristics. Assimilation methods are investigated and blending and optimal interpolation strategies are implemented. Blending assimilation improves error statistics slightly, but the effect of the assimilation is reduced due to noise in the SSM/I motions and is thus not an effective method to improve ice motion estimates. However, optimal interpolation assimilation reduces motion errors by 25--30% over modeled motions and 40--45% over SSM/I motions. Optimal interpolation assimilation is beneficial in all regions, seasons and ice conditions, and is particularly effective in regimes where modeled and SSM/I errors are high. Assimilation alters annual average motion fields. Modeled ice products of ice thickness, ice divergence, Fram Strait ice volume export, transport across the Arctic and interannual basin averages are also influenced by assimilated motions. Assimilation improves estimates of pollutant transport and corrects synoptic-scale errors in the motion fields caused by incorrect forcings or errors in model physics. The portability of the optimal interpolation assimilation method is demonstrated by implementing the strategy in an ice thickness distribution (ITD) model. This research presents an innovative method of combining a new data set of SSM/I-derived ice motions with three different sea ice models via two data assimilation methods. The work described here is the first example of assimilating remotely-sensed data within high-resolution and detailed dynamic-thermodynamic sea ice models. The results demonstrate that assimilation is a valuable resource for determining accurate ice motion in the Arctic.

Incorporating Land-Use Mapping Uncertainty in Remote Sensing Based Calibration of Land-Use Change Models

NASA Astrophysics Data System (ADS)

Cockx, K.; Van de Voorde, T.; Canters, F.; Poelmans, L.; Uljee, I.; Engelen, G.; de Jong, K.; Karssenberg, D.; van der Kwast, J.

2013-05-01

Building urban growth models typically involves a process of historic calibration based on historic time series of land-use maps, usually obtained from satellite imagery. Both the remote sensing data analysis to infer land use and the subsequent modelling of land-use change are subject to uncertainties, which may have an impact on the accuracy of future land-use predictions. Our research aims to quantify and reduce these uncertainties by means of a particle filter data assimilation approach that incorporates uncertainty in land-use mapping and land-use model parameter assessment into the calibration process. This paper focuses on part of this work, more in particular the modelling of uncertainties associated with the impervious surface cover estimation and urban land-use classification adopted in the land-use mapping approach. Both stages are submitted to a Monte Carlo simulation to assess their relative contribution to and their combined impact on the uncertainty in the derived land-use maps. The approach was applied on the central part of the Flanders region (Belgium), using a time-series of Landsat/SPOT-HRV data covering the years 1987, 1996, 2005 and 2012. Although the most likely land-use map obtained from the simulation is very similar to the original classification, it is shown that the errors related to the impervious surface sub-pixel fraction estimation have a strong impact on the land-use map's uncertainty. Hence, incorporating uncertainty in the land-use change model calibration through particle filter data assimilation is proposed to address the uncertainty observed in the derived land-use maps and to reduce uncertainty in future land-use predictions.

Challenges in validating model results for first year ice

NASA Astrophysics Data System (ADS)

Melsom, Arne; Eastwood, Steinar; Xie, Jiping; Aaboe, Signe; Bertino, Laurent

2017-04-01

In order to assess the quality of model results for the distribution of first year ice, a comparison with a product based on observations from satellite-borne instruments has been performed. Such a comparison is not straightforward due to the contrasting algorithms that are used in the model product and the remote sensing product. The implementation of the validation is discussed in light of the differences between this set of products, and validation results are presented. The model product is the daily updated 10-day forecast from the Arctic Monitoring and Forecasting Centre in CMEMS. The forecasts are produced with the assimilative ocean prediction system TOPAZ. Presently, observations of sea ice concentration and sea ice drift are introduced in the assimilation step, but data for sea ice thickness and ice age (or roughness) are not included. The model computes the age of the ice by recording and updating the time passed after ice formation as sea ice grows and deteriorates as it is advected inside the model domain. Ice that is younger than 365 days is classified as first year ice. The fraction of first-year ice is recorded as a tracer in each grid cell. The Ocean and Sea Ice Thematic Assembly Centre in CMEMS redistributes a daily product from the EUMETSAT OSI SAF of gridded sea ice conditions which include "ice type", a representation of the separation of regions between those infested by first year ice, and those infested by multi-year ice. The ice type is parameterized based on data for the gradient ratio GR(19,37) from SSMIS observations, and from the ASCAT backscatter parameter. This product also includes information on ambiguity in the processing of the remote sensing data, and the product's confidence level, which have a strong seasonal dependency.

Estimation of Key Parameters of the Coupled Energy and Water Model by Assimilating Land Surface Data

NASA Astrophysics Data System (ADS)

Abdolghafoorian, A.; Farhadi, L.

2017-12-01

Accurate estimation of land surface heat and moisture fluxes, as well as root zone soil moisture, is crucial in various hydrological, meteorological, and agricultural applications. Field measurements of these fluxes are costly and cannot be readily scaled to large areas relevant to weather and climate studies. Therefore, there is a need for techniques to make quantitative estimates of heat and moisture fluxes using land surface state observations that are widely available from remote sensing across a range of scale. In this work, we applies the variational data assimilation approach to estimate land surface fluxes and soil moisture profile from the implicit information contained Land Surface Temperature (LST) and Soil Moisture (SM) (hereafter the VDA model). The VDA model is focused on the estimation of three key parameters: 1- neutral bulk heat transfer coefficient (CHN), 2- evaporative fraction from soil and canopy (EF), and 3- saturated hydraulic conductivity (Ksat). CHN and EF regulate the partitioning of available energy between sensible and latent heat fluxes. Ksat is one of the main parameters used in determining infiltration, runoff, groundwater recharge, and in simulating hydrological processes. In this study, a system of coupled parsimonious energy and water model will constrain the estimation of three unknown parameters in the VDA model. The profile of SM (LST) at multiple depths is estimated using moisture diffusion (heat diffusion) equation. In this study, the uncertainties of retrieved unknown parameters and fluxes are estimated from the inverse of Hesian matrix of cost function which is computed using the Lagrangian methodology. Analysis of uncertainty provides valuable information about the accuracy of estimated parameters and their correlation and guide the formulation of a well-posed estimation problem. The results of proposed algorithm are validated with a series of experiments using a synthetic data set generated by the simultaneous heat and water (SHAW) model. In addition, the feasibility of extending this algorithm to use remote sensing observations that have low temporal resolution is examined by assimilating the limited number of land surface moisture and temperature observations.

Assimilation of Remotely Sensed Soil Moisture Profiles into a Crop Modeling Framework for Reliable Yield Estimations

NASA Astrophysics Data System (ADS)

Mishra, V.; Cruise, J.; Mecikalski, J. R.

2017-12-01

Much effort has been expended recently on the assimilation of remotely sensed soil moisture into operational land surface models (LSM). These efforts have normally been focused on the use of data derived from the microwave bands and results have often shown that improvements to model simulations have been limited due to the fact that microwave signals only penetrate the top 2-5 cm of the soil surface. It is possible that model simulations could be further improved through the introduction of geostationary satellite thermal infrared (TIR) based root zone soil moisture in addition to the microwave deduced surface estimates. In this study, root zone soil moisture estimates from the TIR based Atmospheric Land Exchange Inverse (ALEXI) model were merged with NASA Soil Moisture Active Passive (SMAP) based surface estimates through the application of informational entropy. Entropy can be used to characterize the movement of moisture within the vadose zone and accounts for both advection and diffusion processes. The Principle of Maximum Entropy (POME) can be used to derive complete soil moisture profiles and, fortuitously, only requires a surface boundary condition as well as the overall mean moisture content of the soil column. A lower boundary can be considered a soil parameter or obtained from the LSM itself. In this study, SMAP provided the surface boundary while ALEXI supplied the mean and the entropy integral was used to tie the two together and produce the vertical profile. However, prior to the merging, the coarse resolution (9 km) SMAP data were downscaled to the finer resolution (4.7 km) ALEXI grid. The disaggregation scheme followed the Soil Evaporative Efficiency approach and again, all necessary inputs were available from the TIR model. The profiles were then assimilated into a standard agricultural crop model (Decision Support System for Agrotechnology, DSSAT) via the ensemble Kalman Filter. The study was conducted over the Southeastern United States for the growing seasons from 2015-2017. Soil moisture profiles compared favorably to in situ data and simulated crop yields compared well with observed yields.

Exploring and Analyzing Climate Variations Online by Using MERRA-2 data at GES DISC

NASA Astrophysics Data System (ADS)

Shen, S.; Ostrenga, D.; Vollmer, B.; Kempler, S.

2016-12-01

NASA Giovanni (Geospatial Interactive Online Visualization ANd aNalysis Infrastructure) (http://giovanni.sci.gsfc.nasa.gov/giovanni/) is a web-based data visualization and analysis system developed by the Goddard Earth Sciences Data and Information Services Center (GES DISC). Current data analysis functions include Lat-Lon map, time series, scatter plot, correlation map, difference, cross-section, vertical profile, and animation etc. The system enables basic statistical analysis and comparisons of multiple variables. This web-based tool facilitates data discovery, exploration and analysis of large amount of global and regional remote sensing and model data sets from a number of NASA data centers. Recently, long term global assimilated atmospheric, land, and ocean data have been integrated into the system that enables quick exploration and analysis of climate data without downloading, and preprocessing the data. Example data include climate reanalysis from NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) which provides data beginning 1980 to present; land data from NASA Global Land Data Assimilation System (GLDAS) which assimilates data from 1948 to 2012; as well as ocean biological data from NASA Ocean Biogeochemical Model (NOBM) which assimilates data from 1998 to 2012. This presentation, using surface air temperature, precipitation, ozone, and aerosol, etc. from MERRA-2, demonstrates climate variation analysis with Giovanni at selected regions.

Comparison of Sequential and Variational Data Assimilation

NASA Astrophysics Data System (ADS)

Alvarado Montero, Rodolfo; Schwanenberg, Dirk; Weerts, Albrecht

2017-04-01

Data assimilation is a valuable tool to improve model state estimates by combining measured observations with model simulations. It has recently gained significant attention due to its potential in using remote sensing products to improve operational hydrological forecasts and for reanalysis purposes. This has been supported by the application of sequential techniques such as the Ensemble Kalman Filter which require no additional features within the modeling process, i.e. it can use arbitrary black-box models. Alternatively, variational techniques rely on optimization algorithms to minimize a pre-defined objective function. This function describes the trade-off between the amount of noise introduced into the system and the mismatch between simulated and observed variables. While sequential techniques have been commonly applied to hydrological processes, variational techniques are seldom used. In our believe, this is mainly attributed to the required computation of first order sensitivities by algorithmic differentiation techniques and related model enhancements, but also to lack of comparison between both techniques. We contribute to filling this gap and present the results from the assimilation of streamflow data in two basins located in Germany and Canada. The assimilation introduces noise to precipitation and temperature to produce better initial estimates of an HBV model. The results are computed for a hindcast period and assessed using lead time performance metrics. The study concludes with a discussion of the main features of each technique and their advantages/disadvantages in hydrological applications.

Assimilating Merged Remote Sensing and Ground based Snowpack Information for Runoff Simulation and Forecasting using Hydrological Models

NASA Astrophysics Data System (ADS)

Infante Corona, J. A.; Lakhankar, T.; Khanbilvardi, R.; Pradhanang, S. M.

2013-12-01

Stream flow estimation and flood prediction influenced by snow melting processes have been studied for the past couple of decades because of their destruction potential, money losses and demises. It has been observed that snow, that was very stationary during its seasons, now is variable in shorter time-scales (daily and hourly) and rapid snowmelt can contribute or been the cause of floods. Therefore, good estimates of snowpack properties on ground are necessary in order to have an accurate prediction of these destructive events. The snow thermal model (SNTHERM) is a 1-dimensional model that analyzes the snowpack properties given the climatological conditions of a particular area. Gridded data from both, in-situ meteorological observations and remote sensing data will be produced using interpolation methods; thus, snow water equivalent (SWE) and snowmelt estimations can be obtained. The soil and water assessment tool (SWAT) is a hydrological model capable of predicting runoff quantity and quality of a watershed given its main physical and hydrological properties. The results from SNTHERM will be used as an input for SWAT in order to have simulated runoff under snowmelt conditions. This project attempts to improve the river discharge estimation considering both, excess rainfall runoff and the snow melting process. Obtaining a better estimation of the snowpack properties and evolution is expected. A coupled use of SNTHERM and SWAT based on meteorological in situ and remote sensed data will improve the temporal and spatial resolution of the snowpack characterization and river discharge estimations, and thus flood prediction.

Neural Network-Based Retrieval of Surface and Root Zone Soil Moisture using Multi-Frequency Remotely-Sensed Observations

NASA Astrophysics Data System (ADS)

Hamed Alemohammad, Seyed; Kolassa, Jana; Prigent, Catherine; Aires, Filipe; Gentine, Pierre

2017-04-01

Knowledge of root zone soil moisture is essential in studying plant's response to different stress conditions since plant photosynthetic activity and transpiration rate are constrained by the water available through their roots. Current global root zone soil moisture estimates are based on either outputs from physical models constrained by observations, or assimilation of remotely-sensed microwave-based surface soil moisture estimates with physical model outputs. However, quality of these estimates are limited by the accuracy of the model representations of physical processes (such as radiative transfer, infiltration, percolation, and evapotranspiration) as well as errors in the estimates of the surface parameters. Additionally, statistical approaches provide an alternative efficient platform to develop root zone soil moisture retrieval algorithms from remotely-sensed observations. In this study, we present a new neural network based retrieval algorithm to estimate surface and root zone soil moisture from passive microwave observations of SMAP satellite (L-band) and AMSR2 instrument (X-band). SMAP early morning observations are ideal for surface soil moisture retrieval. AMSR2 mid-night observations are used here as an indicator of plant hydraulic properties that are related to root zone soil moisture. The combined observations from SMAP and AMSR2 together with other ancillary observations including the Solar-Induced Fluorescence (SIF) estimates from GOME-2 instrument provide necessary information to estimate surface and root zone soil moisture. The algorithm is applied to observations from the first 18 months of SMAP mission and retrievals are validated against in-situ observations and other global datasets.

An Assessment of Capacity, Gaps and Opportunities toward Building a Global Early Warning System for Flood Disasters

NASA Astrophysics Data System (ADS)

Hong, Y.; Adler, R.; Huffman, G.

2007-12-01

Many governmental emergency management agencies or non-governmental organizations need real-time information on emerging disasters for preparedness and response. However, progress in warnings for hydrologic disasters has been constrained by the difficulty of measuring spatiotemporal variability of rainfall fluxes continuously over space and time, due largely to insufficient ground monitoring networks, long delay in data transmission and absence of data sharing protocols among many geopolitically trans-boundary basins. In addition, in-situ gauging stations are often washed away by the very floods they are designed to monitor, making reconstruction of gauges a common post-flood activity around the world. In reality, remote sensing precipitation estimates may be the only source of rainfall information available over much of the globe, particularly for vulnerable countries in the tropics where abundant extreme rain storms and severe flooding events repeat every year. Building on progress in remote sensing technology, researchers have improved the accuracy, coverage, and resolution of rainfall estimates by combining imagery from infrared, passive microwave, and weather radar sensors. Today, remote sensing imagery acquired and processed in real time can provide near-real-time rainfall fluxes at relatively fine spatiotemporal scales (kilometers to tens of kilometers and 30-minute to 3-hour). These new suites of rainfall products have the potential to support daily decision-making in analysis of hydrologic hazards. This talk will address several key issues, including remote sensing rainfall retrieval and data assimilation, for hydrologists to develop alternative satellite-based flood warning systems that may supplement in-situ infrastructure when conventional data sources are denied due to natural or administrative causes. This talk will also assess a module-structure global flood prediction system that has been running at real-time by integrating remote sensing forcing data with simplified hydrological models, in an effort to offer a practical solution to the challenge of building cost-effective flood warning systems for the data-spares regions of the world. The real-time outlook of hazardous floods will quickly disseminate through an open-access web-interface to many agencies and organizations for their daily decision-making, with the potential to save human life and reduce economic impacts. The interactive Web interface will also show close-up maps of the disaster risks overlaid on population or integrated with the Google-Earth visualization tool.

JPL/USC GAIM: Using COSMIC Occultations in a Real-Time Global Ionospheric Data Assimilation Model

NASA Astrophysics Data System (ADS)

Mandrake, L.; Komjathy, A.; Wilson, B. D.; Pi, X.; Hajj, G.; Iijima, B.; Wang, C.

2006-12-01

We are in the midst of a revolution in ionospheric remote sensing driven by the illuminating powers of ground and space-based GPS receivers, new UV remote sensing satellites, and the advent of data assimilation techniques for space weather. In particular, the COSMIC 6-satellite constellation launched in April 2006. COSMIC will provide unprecedented global coverage of GPS occultations (~5000 per day), each of which yields electron density information with unprecedented ~1 km vertical resolution. Calibrated measurements of ionospheric delay (total electron content or TEC) suitable for input into assimilation models will be available in near real-time (NRT) from the COSMIC project with a latency of 30 to 120 minutes. Similarly, NRT TEC data are available from two worldwide NRT networks of ground GPS receivers (~75 5-minute sites and ~125 more hourly sites, operated by JPL and others). The combined NRT ground and space-based GPS datasets provide a new opportunity to more accurately specify the 3-dimensional ionospheric density with a time lag of only 15 to 120 minutes. With the addition of the vertically-resolved NRT occultation data, the retrieved profile shapes will model the hour-to-hour ionospheric "weather" much more accurately. The University of Southern California (USC) and the Jet Propulsion Laboratory (JPL) have jointly developed a real-time Global Assimilative Ionospheric Model (GAIM) to monitor space weather, study storm effects, and provide ionospheric calibration for DoD customers and NASA flight projects. JPL/USC GAIM is a physics- based 3D data assimilation model that uses both 4DVAR and Kalman filter techniques to solve for the ion & electron density state and key drivers such as equatorial electrodynamics, neutral winds, and production terms. Daily (delayed) GAIM runs can accept as input ground GPS TEC data from 1000+ sites, occultation links from CHAMP, SAC-C, and the COSMIC constellation, UV limb and nadir scans from the TIMED and DMSP satellites, and in situ data from a variety of satellites (DMSP and C/NOFS). RTGAIM ingests multiple data sources in real time, updates the 3D electron density grid every 5 minutes, and solves for improved drivers every 1-2 hours. Since our forward physics model and the adjoint model were expressly designed for data assimilation and computational efficiency, all of this can be accomplished on a single dual-processor Unix workstation. Customers are currently evaluating the accuracy of JPL/USC GAIM "nowcasts" for ray tracing applications and trans-ionospheric path delay calibration. In the talk, we will discuss the expected impact of COSMIC occultation data; show first results for ingest of COSMIC data using the GAIM Kalman filter; present validation of the GAIM electron density grid by comparisons to Abel profiles and independent datasets; describe recent improvements to the JPL/USC GAIM model; and describe our plans for NRT ingest of COSMIC data into RTGAIM.

ASCAT soil moisture data assimilation through the Ensemble Kalman Filter for improving streamflow simulation in Mediterranean catchments

NASA Astrophysics Data System (ADS)

Loizu, Javier; Massari, Christian; Álvarez-Mozos, Jesús; Casalí, Javier; Goñi, Mikel

2016-04-01

Assimilation of Surface Soil Moisture (SSM) observations obtained from remote sensing techniques have been shown to improve streamflow prediction at different time scales of hydrological modeling. Different sensors and methods have been tested for their application in SSM estimation, especially in the microwave region of the electromagnetic spectrum. The available observation devices include passive microwave sensors such as the Advanced Microwave Scanning Radiometer - Earth Observation System (AMSR-E) onboard the Aqua satellite and the Soil Moisture and Ocean Salinity (SMOS) mission. On the other hand, active microwave systems include Scatterometers (SCAT) onboard the European Remote Sensing satellites (ERS-1/2) and the Advanced Scatterometer (ASCAT) onboard MetOp-A satellite. Data assimilation (DA) include different techniques that have been applied in hydrology and other fields for decades. These techniques include, among others, Kalman Filtering (KF), Variational Assimilation or Particle Filtering. From the initial KF method, different techniques were developed to suit its application to different systems. The Ensemble Kalman Filter (EnKF), extensively applied in hydrological modeling improvement, shows its capability to deal with nonlinear model dynamics without linearizing model equations, as its main advantage. The objective of this study was to investigate whether data assimilation of SSM ASCAT observations, through the EnKF method, could improve streamflow simulation of mediterranean catchments with TOPLATS hydrological complex model. The DA technique was programmed in FORTRAN, and applied to hourly simulations of TOPLATS catchment model. TOPLATS (TOPMODEL-based Land-Atmosphere Transfer Scheme) was applied on its lumped version for two mediterranean catchments of similar size, located in northern Spain (Arga, 741 km2) and central Italy (Nestore, 720 km2). The model performs a separated computation of energy and water balances. In those balances, the soil is divided into two layers, the upper Surface Zone (SZ), and the deeper Transmission Zone (TZ). In this study, the SZ depth was fixed to 5 cm, for adequate assimilation of observed data. Available data was distributed as follows: first, the model was calibrated for the 2001-2007 period; then the 2007-2010 period was used for satellite data rescaling purposes. Finally, data assimilation was applied during the validation (2010-2013) period. Application of the EnKF required the following steps: 1) rescaling of satellite data, 2) transformation of rescaled data into Soil Water Index (SWI) through a moving average filter, where a T = 9 calibrated value was applied, 3) generation of a 50 member ensemble through perturbation of inputs (rainfall and temperature) and three selected parameters, 4) validation of the ensemble through the compliance of two criteria based on ensemble's spread, mean square error and skill and, 5) Kalman Gain calculation. In this work, comparison of three satellite data rescaling techniques: 1) cumulative distribution Function (CDF) matching, 2) variance matching and 3) linear least square regression was also performed. Results obtained in this study showed slight improvements of hourly Nash-Sutcliffe Efficiency (NSE) in both catchments, with the different rescaling methods evaluated. Larger improvements were found in terms of seasonal simulated volume error reduction.

Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction

NASA Astrophysics Data System (ADS)

Baatz, Roland; Hendricks Franssen, Harrie-Jan; Han, Xujun; Hoar, Tim; Reemt Bogena, Heye; Vereecken, Harry

2017-05-01

In situ soil moisture sensors provide highly accurate but very local soil moisture measurements, while remotely sensed soil moisture is strongly affected by vegetation and surface roughness. In contrast, cosmic-ray neutron sensors (CRNSs) allow highly accurate soil moisture estimation on the field scale which could be valuable to improve land surface model predictions. In this study, the potential of a network of CRNSs installed in the 2354 km2 Rur catchment (Germany) for estimating soil hydraulic parameters and improving soil moisture states was tested. Data measured by the CRNSs were assimilated with the local ensemble transform Kalman filter in the Community Land Model version 4.5. Data of four, eight and nine CRNSs were assimilated for the years 2011 and 2012 (with and without soil hydraulic parameter estimation), followed by a verification year 2013 without data assimilation. This was done using (i) a regional high-resolution soil map, (ii) the FAO soil map and (iii) an erroneous, biased soil map as input information for the simulations. For the regional soil map, soil moisture characterization was only improved in the assimilation period but not in the verification period. For the FAO soil map and the biased soil map, soil moisture predictions improved strongly to a root mean square error of 0.03 cm3 cm-3 for the assimilation period and 0.05 cm3 cm-3 for the evaluation period. Improvements were limited by the measurement error of CRNSs (0.03 cm3 cm-3). The positive results obtained with data assimilation of nine CRNSs were confirmed by the jackknife experiments with four and eight CRNSs used for assimilation. The results demonstrate that assimilated data of a CRNS network can improve the characterization of soil moisture content on the catchment scale by updating spatially distributed soil hydraulic parameters of a land surface model.

Land Surface Data Assimilation

NASA Astrophysics Data System (ADS)

Houser, P. R.

2012-12-01

Information about land surface water, energy and carbon conditions is of critical importance to real-world applications such as agricultural production, water resource management, flood prediction, water supply, weather and climate forecasting, and environmental preservation. While ground-based observational networks are improving, the only practical way to observe these land surface states on continental to global scales is via satellites. Remote sensing can make spatially comprehensive measurements of various components of the terrestrial system, but it cannot provide information on the entire system (e.g. evaporation), and the observations represent only an instant in time. Land surface process models may be used to predict temporal and spatial terrestrial dynamics, but these predictions are often poor, due to model initialization, parameter and forcing, and physics errors. Therefore, an attractive prospect is to combine the strengths of land surface models and observations (and minimize the weaknesses) to provide a superior terrestrial state estimate. This is the goal of land surface data assimilation. Data Assimilation combines observations into a dynamical model, using the model's equations to provide time continuity and coupling between the estimated fields. Land surface data assimilation aims to utilize both our land surface process knowledge, as embodied in a land surface model, and information that can be gained from observations. Both model predictions and observations are imperfect and we wish to use both synergistically to obtain a more accurate result. Moreover, both contain different kinds of information, that when used together, provide an accuracy level that cannot be obtained individually. Model biases can be mitigated using a complementary calibration and parameterization process. Limited point measurements are often used to calibrate the model(s) and validate the assimilation results. This presentation will provide a brief background on land surface observation, modeling and data assimilation, followed by a discussion of various hydrologic data assimilation challenges, and finally conclude with several land surface data assimilation case studies.

Hydrologic data assimilation with a hillslope-scale-resolving model and L band radar observations: Synthetic experiments with the ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Flores, Alejandro N.; Bras, Rafael L.; Entekhabi, Dara

2012-08-01

Soil moisture information is critical for applications like landslide susceptibility analysis and military trafficability assessment. Existing technologies cannot observe soil moisture at spatial scales of hillslopes (e.g., 100 to 102 m) and over large areas (e.g., 102 to 105 km2) with sufficiently high temporal coverage (e.g., days). Physics-based hydrologic models can simulate soil moisture at the necessary spatial and temporal scales, albeit with error. We develop and test a data assimilation framework based on the ensemble Kalman filter for constraining uncertain simulated high-resolution soil moisture fields to anticipated remote sensing products, specifically NASA's Soil Moisture Active-Passive (SMAP) mission, which will provide global L band microwave observation approximately every 2-3 days. The framework directly assimilates SMAP synthetic 3 km radar backscatter observations to update hillslope-scale bare soil moisture estimates from a physics-based model. Downscaling from 3 km observations to hillslope scales is achieved through the data assimilation algorithm. Assimilation reduces bias in near-surface soil moisture (e.g., top 10 cm) by approximately 0.05 m3/m3and expected root-mean-square errors by at least 60% in much of the watershed, relative to an open loop simulation. However, near-surface moisture estimates in channel and valley bottoms do not improve, and estimates of profile-integrated moisture throughout the watershed do not substantially improve. We discuss the implications of this work, focusing on ongoing efforts to improve soil moisture estimation in the entire soil profile through joint assimilation of other satellite (e.g., vegetation) and in situ soil moisture measurements.

Impact of different satellite soil moisture products on the predictions of a continuous distributed hydrological model

NASA Astrophysics Data System (ADS)

Laiolo, P.; Gabellani, S.; Campo, L.; Silvestro, F.; Delogu, F.; Rudari, R.; Pulvirenti, L.; Boni, G.; Fascetti, F.; Pierdicca, N.; Crapolicchio, R.; Hasenauer, S.; Puca, S.

2016-06-01

The reliable estimation of hydrological variables in space and time is of fundamental importance in operational hydrology to improve the flood predictions and hydrological cycle description. Nowadays remotely sensed data can offer a chance to improve hydrological models especially in environments with scarce ground based data. The aim of this work is to update the state variables of a physically based, distributed and continuous hydrological model using four different satellite-derived data (three soil moisture products and a land surface temperature measurement) and one soil moisture analysis to evaluate, even with a non optimal technique, the impact on the hydrological cycle. The experiments were carried out for a small catchment, in the northern part of Italy, for the period July 2012-June 2013. The products were pre-processed according to their own characteristics and then they were assimilated into the model using a simple nudging technique. The benefits on the model predictions of discharge were tested against observations. The analysis showed a general improvement of the model discharge predictions, even with a simple assimilation technique, for all the assimilation experiments; the Nash-Sutcliffe model efficiency coefficient was increased from 0.6 (relative to the model without assimilation) to 0.7, moreover, errors on discharge were reduced up to the 10%. An added value to the model was found in the rainfall season (autumn): all the assimilation experiments reduced the errors up to the 20%. This demonstrated that discharge prediction of a distributed hydrological model, which works at fine scale resolution in a small basin, can be improved with the assimilation of coarse-scale satellite-derived data.

Impact of the assimilation of satellite soil moisture and LST on the hydrological cycle

NASA Astrophysics Data System (ADS)

Laiolo, Paola; Gabellani, Simone; Delogu, Fabio; Silvestro, Francesco; Rudari, Roberto; Campo, Lorenzo; Boni, Giorgio

2014-05-01

The reliable estimation of hydrological variables (e.g. soil moisture, evapotranspiration, surface temperature) in space and time is of fundamental importance in operational hydrology to improve the forecast of the rainfall-runoff response of catchments and, consequently, flood predictions. Nowadays remote sensing can offer a chance to provide good space-time estimates of several hydrological variables and then improve hydrological model performances especially in environments with scarce ground based data. The aim of this work is to investigate the impacts on the performances of a distributed hydrological model (Continuum) of the assimilation of satellite-derived soil moisture products and Land Surface (LST). In this work three different soil moisture (SM) products, derived by ASCAT sensor, are used. These data are provided by the EUMETSAT's H-SAF (Satellite Application Facility on Support to Operational Hydrology and Water Management) program. The considered soil moisture products are: large scale surface soil moisture (SM OBS 1 - H07), small scale surface soil moisture (SM OBS 2 - H08) and profile index in the roots region (SM DAS 2 - H14). These data are compared with soil moisture estimated by Continuum model on the Orba catchment (800 km2), in the northern part of Italy, for the period July 2012-June 2013. Different assimilation experiments have been performed. The first experiment consists in the assimilation of the SM products by using a simple Nudging technique; the second one is the assimilation of only LST data, derived from MSG satellite, and the third is the assimilation of both SM products and LST. The benefits on the model predictions of discharge, LST and soil moisture dynamics were tested.

Assimilating satellite-based canopy height within an ecosystem model to estimate aboveground forest biomass

NASA Astrophysics Data System (ADS)

Joetzjer, E.; Pillet, M.; Ciais, P.; Barbier, N.; Chave, J.; Schlund, M.; Maignan, F.; Barichivich, J.; Luyssaert, S.; Hérault, B.; von Poncet, F.; Poulter, B.

2017-07-01

Despite advances in Earth observation and modeling, estimating tropical biomass remains a challenge. Recent work suggests that integrating satellite measurements of canopy height within ecosystem models is a promising approach to infer biomass. We tested the feasibility of this approach to retrieve aboveground biomass (AGB) at three tropical forest sites by assimilating remotely sensed canopy height derived from a texture analysis algorithm applied to the high-resolution Pleiades imager in the Organizing Carbon and Hydrology in Dynamic Ecosystems Canopy (ORCHIDEE-CAN) ecosystem model. While mean AGB could be estimated within 10% of AGB derived from census data in average across sites, canopy height derived from Pleiades product was spatially too smooth, thus unable to accurately resolve large height (and biomass) variations within the site considered. The error budget was evaluated in details, and systematic errors related to the ORCHIDEE-CAN structure contribute as a secondary source of error and could be overcome by using improved allometric equations.

The South American Land Data Assimilation System (SALDAS) 5-Year Retrospective Atmospheric Forcing Datasets

NASA Technical Reports Server (NTRS)

deGoncalves, Luis Gustavo G.; Shuttleworth, William J.; Vila, Daniel; Larroza, Elaine; Bottino, Marcus J.; Herdies, Dirceu L.; Aravequia, Jose A.; De Mattos, Joao G. Z.; Toll, David L.; Rodell, Matthew;

Progress in and prospects for fluvial flood modelling.

PubMed

Wheater, H S

2002-07-15

Recent floods in the UK have raised public and political awareness of flood risk. There is an increasing recognition that flood management and land-use planning are linked, and that decision-support modelling tools are required to address issues of climate and land-use change for integrated catchment management. In this paper, the scientific context for fluvial flood modelling is discussed, current modelling capability is considered and research challenges are identified. Priorities include (i) appropriate representation of spatial precipitation, including scenarios of climate change; (ii) development of a national capability for continuous hydrological simulation of ungauged catchments; (iii) improved scientific understanding of impacts of agricultural land-use and land-management change, and the development of new modelling approaches to represent those impacts; (iv) improved representation of urban flooding, at both local and catchment scale; (v) appropriate parametrizations for hydraulic simulation of in-channel and flood-plain flows, assimilating available ground observations and remotely sensed data; and (vi) a flexible decision-support modelling framework, incorporating developments in computing, data availability, data assimilation and uncertainty analysis.

CLAES Product Improvement by use of GSFC Data Assimilation System

NASA Technical Reports Server (NTRS)

Kumer, J. B.; Douglass, Anne (Technical Monitor)

2001-01-01

Recent development in chemistry transport models (CTM) and in data assimilation systems (DAS) indicate impressive predictive capability for the movement of airparcels and the chemistry that goes on within these. This project was aimed at exploring the use of this capability to achieve improved retrieval of geophysical parameters from remote sensing data. The specific goal was to improve retrieval of the CLAES CH4 data obtained during the active north high latitude dynamics event of 18 to 25 February 1992. The model capabilities would be used: (1) rather than climatology to improve on the first guess and the a-priori fields, and (2) to provide horizontal gradients to include in the retrieval forward model. The retrieval would be implemented with the first forward DAS prediction. The results would feed back to the DAS and a second DAS prediction for first guess, a-priori and gradients would feed to the retrieval. The process would repeat to convergence and then proceed to the next day.

Application of SAR remote sensing and crop modeling for operational rice crop monitoring in South and South East Asian Countries

NASA Astrophysics Data System (ADS)

Setiyono, T. D.; Holecz, F.; Khan, N. I.; Barbieri, M.; Maunahan, A. A.; Gatti, L.; Quicho, E. D.; Pazhanivelan, S.; Campos-Taberner, M.; Collivignarelli, F.; Haro, J. G.; Intrman, A.; Phuong, D.; Boschetti, M.; Prasadini, P.; Busetto, L.; Minh, V. Q.; Tuan, V. Q.

2017-12-01

This study uses multi-temporal SAR imagery, automated image processing, rule-based classification and field observations to classify rice in multiple locations in South and South Asian countries and assimilate the information into ORYZA Crop Growth Simulation Model (CGSM) to monitor rice yield. The study demonstrates examples of operational application of this rice monitoring system in: (1) detecting drought impact on rice planting in Central Thailand and Tamil Nadu, India, (2) mapping heat stress impact on rice yield in Andhra Pradesh, India, and (3) generating historical rice yield data for districts in Red River Delta, Vietnam.

A model of regional primary production for use with coarse resolution satellite data

NASA Technical Reports Server (NTRS)

Prince, S. D.

1991-01-01

A model of crop primary production, which was originally developed to relate the amount of absorbed photosynthetically active radiation (APAR) to net production in field studies, is discussed in the context of coarse resolution regional remote sensing of primary production. The model depends on an approximately linear relationship between APAR and the normalized difference vegetation index. A more comprehensive form of the conventional model is shown to be necessary when different physiological types of plants or heterogeneous vegetation types occur within the study area. The predicted variable in the new model is total assimilation (net production plus respiration) rather than net production alone or harvest yield.

Snowpack modeling in the context of radiance assimilation for snow water equivalent mapping

NASA Astrophysics Data System (ADS)

Durand, M. T.; Kim, R. S.; Li, D.; Dumont, M.; Margulis, S. A.

2017-12-01

Data assimilation is often touted as a means of overcoming deficiences in both snowpack modeling and snowpack remote sensing. Direct assimilation of microwave radiances, rather than assimilating microwave retrievals, has shown promise, in this context. This is especially the case for deep mountain snow, which is often assumed to be infeasible to measure with microwave measurements, due to saturation issues. We first demonstrate that the typical way of understanding saturation has often been misunderstood. We show that deep snow leads to a complex microwave signature, but not to saturation per se, because of snowpack stratigraphy. This explains why radiance assimilation requires detailed snowpack models that adequatley stratgigraphy to function accurately. We examine this with two case studies. First, we show how the CROCUS predictions of snowpack stratigraphy allows for assimilation of airborne passive microwave measurements over three 1km2 CLPX Intensive Study Areas. Snowpack modeling and particle filter analysis is performed at 120 m spatial resolution. When run without the benefit of radiance assimilation, CROCUS does not fully capture spatial patterns in the data (R2=0.44; RMSE=26 cm). Assimlilation of microwave radiances for a single flight recovers the spatial pattern of snow depth (R2=0.85; RMSE = 13 cm). This is despite the presence of deep snow; measured depths range from 150 to 325 cm. Adequate results are obtained even for partial forest cover, and bias in precipitation forcing. The results are severely degraded if a three-layer snow model is used, however. The importance of modeling snowpack stratigraphy is highlighted. Second, we compare this study to a recent analysis assimilating spaceborne radiances for a 511 km2 sub-watershed of the Kern River, in the Sierra Nevada. Here, the daily Level 2A AMSR-E footprints (88 km2) are assimilated into a model running at 90 m spatial resolution. The three-layer model is specially adapted to predict "effective" stratigraphy. We compare and contrast these approaches to modeling snowpack stratigraphy, and highlight the critical role of models in radiance assimilation schemes.

Basic technologies of web services framework for research, discovery, and processing the disparate massive Earth observation data from heterogeneous sources

NASA Astrophysics Data System (ADS)

Savorskiy, V.; Lupyan, E.; Balashov, I.; Burtsev, M.; Proshin, A.; Tolpin, V.; Ermakov, D.; Chernushich, A.; Panova, O.; Kuznetsov, O.; Vasilyev, V.

2014-04-01

Both development and application of remote sensing involves a considerable expenditure of material and intellectual resources. Therefore, it is important to use high-tech means of distribution of remote sensing data and processing results in order to facilitate access for as much as possible number of researchers. It should be accompanied with creation of capabilities for potentially more thorough and comprehensive, i.e. ultimately deeper, acquisition and complex analysis of information about the state of Earth's natural resources. As well objective need in a higher degree of Earth observation (EO) data assimilation is set by conditions of satellite observations, in which the observed objects are uncontrolled state. Progress in addressing this problem is determined to a large extent by order of the distributed EO information system (IS) functioning. Namely, it is largely dependent on reducing the cost of communication processes (data transfer) between spatially distributed IS nodes and data users. One of the most effective ways to improve the efficiency of data exchange processes is the creation of integrated EO IS optimized for running procedures of distributed data processing. The effective EO IS implementation should be based on specific software architecture.

Propagation Limitations in Remote Sensing.

DTIC Science & Technology

Contents: Multi-sensors and systems in remote sensing ; Radar sensing systems over land; Remote sensing techniques in oceanography; Influence of...propagation media and background; Infrared techniques in remote sensing ; Photography in remote sensing ; Analytical studies in remote sensing .

Impact of Atmospheric Infrared Sounder (AIRS) Thermodynamic Profiles on Regional Precipitation Forecasting

NASA Technical Reports Server (NTRS)

Chou, S.-H.; Zavodsky, B. T.; Jedloved, G. J.

2010-01-01

In data sparse regions, remotely-sensed observations can be used to improve analyses and lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles in clear and cloudy regions with accuracy which approaches that of radiosondes. The purpose of this paper is to describe an approach to assimilate AIRS thermodynamic profile data into a regional configuration of the Advanced Research WRF (ARW) model using WRF-Var. Quality indicators are used to select only the highest quality temperature and moisture profiles for assimilation in clear and partly cloudy regions, and uncontaminated portions of retrievals above clouds in overcast regions. Separate error characteristics for land and water profiles are also used in the assimilation process. Assimilation results indicate that AIRS profiles produce an analysis closer to in situ observations than the background field. Forecasts from a 37-day case study period in the winter of 2007 show that AIRS profile data can lead to improvements in 6-h cumulative precipitation forecasts resulting from improved thermodynamic fields. Additionally, in a convective heavy rainfall event from February 2007, assimilation of AIRS profiles produces a more unstable boundary layer resulting in enhanced updrafts in the model. These updrafts produce a squall line and precipitation totals that more closely reflect ground-based observations than a no AIRS control forecast. The location of available high-quality AIRS profiles ahead of approaching storm systems is found to be of paramount importance to the amount of impact the observations will have on the resulting forecasts.

Satellite-guided hydro-economic analysis for integrated management and prediction of the impact of droughts on agricultural regions

NASA Astrophysics Data System (ADS)

Maneta, M. P.; Howitt, R.; Kimball, J. S.

2013-12-01

Agricultural activity can exacerbate or buffer the impact of climate variability, especially droughts, on the hydrologic and socioeconomic conditions of rural areas. Potential negative regional impacts of droughts include impoverishment of agricultural regions, deterioration or overuse of water resources, risk of monoculture, and regional dependence on external food markets. Policies that encourage adequate management practices in the face of adverse climatic events are critical to preserve rural livelihoods and to ensure a sustainable future for agriculture. Diagnosing and managing drought effects on agricultural production, on the social and natural environment, and on limited water resources, is highly complex and interdisciplinary. The challenges that decision-makers face to mitigate the impact of water shortage are social, agronomic, economic and environmental in nature and therefore must be approached from an integrated multidisciplinary point of view. Existing observation technologies, in conjunction with models and assimilation methods open the opportunity for novel interdisciplinary analysis tools to support policy and decision making. We present an integrated modeling and observation framework driven by satellite remote sensing and other ancillary information from regional monitoring networks to enable robust regional assessment and prediction of drought impacts on agricultural production, water resources, management decisions and socioeconomic policy. The core of this framework is a hydroeconomic model of agricultural production that assimilates remote sensing inputs to quantify the amount of land, water, fertilizer and labor farmers allocate for each crop they choose to grow on a seasonal basis in response to changing climatic conditions, including drought. A regional hydroclimatologic model provides biophysical constraints to an economic model of agricultural production based on a class of models referred to as positive mathematical programming (PMP). A recursive Bayesian update method is used to adjust the model parameters by assimilating information on crop acreage, production, and crop evapotranspiration estimated from high-spatial resolution satellite remote sensing. We are developing new land parameter records adapted for agricultural application by merging relatively fine scale, calibrated spectral reflectance time series with similar spectral information from coarser scale and more temporally continuous global satellite data records. These new products will be used to generate field scale estimates of LAI and FPAR, which will be used with regional surface meteorology and biophysical data to estimate crop production including C4 crop types. This integrated framework provides an operational means to monitor and forecast what crops will be grown and how farmers will allocate land, water and other agricultural resources under expected adverse conditions, and the resulting consequences for other water users. It will also permit evaluation of impacts of water policy and changes in food prices on rural community livelihoods. The Bayesian update framework constitutes an efficient method for the identification of the production function parameters and provides valuable information on the associated uncertainty of the forecasts.

A satellite-driven, client-server hydro-economic model prototype for agricultural water management

NASA Astrophysics Data System (ADS)

Maneta, Marco; Kimball, John; He, Mingzhu; Payton Gardner, W.

2017-04-01

Anticipating agricultural water demand, land reallocation, and impact on farm revenues associated with different policy or climate constraints is a challenge for water managers and for policy makers. While current integrated decision support systems based on programming methods provide estimates of farmer reaction to external constraints, they have important shortcomings such as the high cost of data collection surveys necessary to calibrate the model, biases associated with inadequate farm sampling, infrequent model updates and recalibration, model overfitting, or their deterministic nature, among other problems. In addition, the administration of water supplies and the generation of policies that promote sustainable agricultural regions depend on more than one bureau or office. Unfortunately, managers from local and regional agencies often use different datasets of variable quality, which complicates coordinated action. To overcome these limitations, we present a client-server, integrated hydro-economic modeling and observation framework driven by satellite remote sensing and other ancillary information from regional monitoring networks. The core of the framework is a stochastic data assimilation system that sequentially ingests remote sensing observations and corrects the parameters of the hydro-economic model at unprecedented spatial and temporal resolutions. An economic model of agricultural production, based on mathematical programming, requires information on crop type and extent, crop yield, crop transpiration and irrigation technology. A regional hydro-climatologic model provides biophysical constraints to an economic model of agricultural production with a level of detail that permits the study of the spatial impact of large- and small-scale water use decisions. Crop type and extent is obtained from the Cropland Data Layer (CDL), which is multi-sensor operational classification of crops maintained by the United States Department of Agriculture. Because this product is only available for the conterminous United States, the framework is currently only applicable in this region. To obtain information on crop phenology, productivity and transpiration at adequate spatial and temporal frequencies we blend high spatial resolution Landsat information with high temporal fidelity MODIS imagery. The result is a 30 m, 8-day fused dataset of crop greenness that is subsequently transformed into productivity and transpiration by adapting existing forest productivity and transpiration algorithms for agricultural applications. To ensure all involved agencies work with identical information and that end-users are sheltered from the computational burden of storing and processing remote sensing data, this modeling framework is integrated in a client-server architecture based on the Hydra platform (www.hydraplatform.org). Assimilation and processing of resource-intensive remote sensing information, as well as hydrologic and other ancillary data, occur on the server side. With this architecture, our decision support system becomes a light weight 'app' that connects to the server to retrieve the latest information regarding water demands, land use, yields and hydrologic information required to run different management scenarios. This architecture ensures that all agencies and teams involved in water management use the same, up-to-date information in their simulations.

Earth view: A business guide to orbital remote sensing

NASA Technical Reports Server (NTRS)

Bishop, Peter C.

1990-01-01

The following subject areas are covered: Earth view - a guide to orbital remote sensing; current orbital remote sensing systems (LANDSAT, SPOT image, MOS-1, Soviet remote sensing systems); remote sensing satellite; and remote sensing organizations.

Contribution Of The SWOT Mission To Large-Scale Hydrological Modeling Using Data Assimilation

NASA Astrophysics Data System (ADS)

Emery, C. M.; Biancamaria, S.; Boone, A. A.; Ricci, S. M.; Rochoux, M. C.; Garambois, P. A.; Paris, A.; Calmant, S.

2016-12-01

The purpose of this work is to improve water fluxes estimation on the continental surfaces, at interanual and interseasonal scale (from few years to decennial time period). More specifically, it studies contribution of the incoming SWOT satellite mission to improve hydrology model at global scale, and using the land surface model ISBA-TRIP. This model corresponds to the continental component of the CNRM (French meteorological research center)'s climatic model. This study explores the potential of satellite data to correct either input parameters of the river routing scheme TRIP or its state variables. To do so, a data assimilation platform (using an Ensemble Kalman Filter, EnKF) has been implemented to assimilate SWOT virtual observations as well as discharges estimated from real nadir altimetry data. A series of twin experiments is used to test and validate the parameter estimation module of the platform. SWOT virtual-observations of water heights along SWOT tracks (with a 10 cm white noise model error) are assimilated to correct the river routing model parameters. To begin with, we chose to focus exclusively on the river manning coefficient, with the possibility to easily extend to other parameters such as the river widths. First results show that the platform is able to recover the "true" Manning distribution assimilating SWOT-like water heights. The error on the coefficients goes from 35 % before assimilation to 9 % after four SWOT orbit repeat period of 21 days. In the state estimation mode, daily assimilation cycles are realized to correct TRIP river water storage initial state by assimilating ENVISAT-based discharge. Those observations are derived from ENVISAT water elevation measures, using rating curves from the MGB-IPH hydrological model (calibrated over the Amazon using in situ gages discharge). Using such kind of observation allows going beyond idealized twin experiments and also to test contribution of a remotely-sensed discharge product, which could prefigure the SWOT discharge product. The results show that discharge after assimilation are globally improved : the root-mean-square error between the analysis discharge ensemble mean and in situ discharges is reduced by 30 %, compared to the root-mean-square error between the free run and in situ discharges.

Preliminary Results from an Assimilation of Saharan Dust Using TOMS Radiances and the GOCART Model

NASA Technical Reports Server (NTRS)

Weaver, C. J.; daSilva, Arlindo; Ginoux, Paul; Torres, Omar; Einaudi, Franco (Technical Monitor)

2000-01-01

At NASA Goddard we are developing a global aerosol data assimilation system that combines advances in remote sensing and modeling of atmospheric aerosols. The goal is to provide high resolution, 3-D aerosol distributions to the research community. Our first step is to develop a simple assimilation system for Saharan mineral aerosol. The Goddard Chemistry and Aerosol Radiation model (GOCART) provides accurate 3-D mineral aerosol size distributions. Surface mobilization, wet and dry deposition, convective and long-range transport are all driven by assimilated fields from the Goddard Earth Observing System Data Assimilation System, GEOS-DAS. Our version of GOCART transports sizes from .08-10 microns and only simulates Saharan dust. We draw the assimilation to two observables in this study: the TOMS aerosol index (Al) which is directly related to the ratio of the 340 and 380 radiances and the 380 radiance alone. The forward model that simulates the observables requires the aerosol optical thickness, the single scattering albedo and the height of the aerosol layer from the GOCART fields. The forward model also requires a refractive index for the dust. We test three index values to see which best fits the TOMS observables. These are 1) for Saharan dust reported by Patterson, 2) for a mixture of Saharan dust and a highly reflective material (sea salt or sulfate) and 3) for pure illite. The assimilation works best assuming either pure illite or the dust mixture. Our assimilation cycle first determines values of the aerosol index (Al) and the radiance at 380 nm based on the GOCART aerosol fields. Differences between the observed and GOCART model calculated Al and 380 nm radiance are first analyzed horizontally using the Physical-space Statistical Analysis System (PSAS). A quasi-Newton iteration is then performed to produce analyzed 3D aerosol fields according to parameterized background and observation error covariances. We only assimilate observations into the the GOCART model over regions of Africa and the Atlantic where mineral aerosols are dominant and carbonaceous aerosols are minimal.

Technology study of quantum remote sensing imaging

NASA Astrophysics Data System (ADS)

Bi, Siwen; Lin, Xuling; Yang, Song; Wu, Zhiqiang

2016-02-01

According to remote sensing science and technology development and application requirements, quantum remote sensing is proposed. First on the background of quantum remote sensing, quantum remote sensing theory, information mechanism, imaging experiments and prototype principle prototype research situation, related research at home and abroad are briefly introduced. Then we expounds compress operator of the quantum remote sensing radiation field and the basic principles of single-mode compression operator, quantum quantum light field of remote sensing image compression experiment preparation and optical imaging, the quantum remote sensing imaging principle prototype, Quantum remote sensing spaceborne active imaging technology is brought forward, mainly including quantum remote sensing spaceborne active imaging system composition and working principle, preparation and injection compression light active imaging device and quantum noise amplification device. Finally, the summary of quantum remote sensing research in the past 15 years work and future development are introduced.

Using Air Temperature to Quantitatively Predict the MODIS Fractional Snow Cover Retrieval Errors over the Continental US (CONUS)

NASA Technical Reports Server (NTRS)

Dong, Jiarui; Ek, Mike; Hall, Dorothy K.; Peters-Lidard, Christa; Cosgrove, Brian; Miller, Jeff; Riggs, George A.; Xia, Youlong

2013-01-01

In the middle to high latitude and alpine regions, the seasonal snow pack can dominate the surface energy and water budgets due to its high albedo, low thermal conductivity, high emissivity, considerable spatial and temporal variability, and ability to store and then later release a winters cumulative snowfall (Cohen, 1994; Hall, 1998). With this in mind, the snow drought across the U.S. has raised questions about impacts on water supply, ski resorts and agriculture. Knowledge of various snow pack properties is crucial for short-term weather forecasts, climate change prediction, and hydrologic forecasting for producing reliable daily to seasonal forecasts. One potential source of this information is the multi-institution North American Land Data Assimilation System (NLDAS) project (Mitchell et al., 2004). Real-time NLDAS products are used for drought monitoring to support the National Integrated Drought Information System (NIDIS) and as initial conditions for a future NCEP drought forecast system. Additionally, efforts are currently underway to assimilate remotely-sensed estimates of land-surface states such as snowpack information into NLDAS. It is believed that this assimilation will not only produce improved snowpack states that better represent snow evolving conditions, but will directly improve the monitoring of drought.

Large-scale hydrological model river storage and discharge correction using a satellite altimetry-based discharge product

NASA Astrophysics Data System (ADS)

Emery, Charlotte Marie; Paris, Adrien; Biancamaria, Sylvain; Boone, Aaron; Calmant, Stéphane; Garambois, Pierre-André; Santos da Silva, Joecila

2018-04-01

Land surface models (LSMs) are widely used to study the continental part of the water cycle. However, even though their accuracy is increasing, inherent model uncertainties can not be avoided. In the meantime, remotely sensed observations of the continental water cycle variables such as soil moisture, lakes and river elevations are more frequent and accurate. Therefore, those two different types of information can be combined, using data assimilation techniques to reduce a model's uncertainties in its state variables or/and in its input parameters. The objective of this study is to present a data assimilation platform that assimilates into the large-scale ISBA-CTRIP LSM a punctual river discharge product, derived from ENVISAT nadir altimeter water elevation measurements and rating curves, over the whole Amazon basin. To deal with the scale difference between the model and the observation, the study also presents an initial development for a localization treatment that allows one to limit the impact of observations to areas close to the observation and in the same hydrological network. This assimilation platform is based on the ensemble Kalman filter and can correct either the CTRIP river water storage or the discharge. Root mean square error (RMSE) compared to gauge discharges is globally reduced until 21 % and at Óbidos, near the outlet, RMSE is reduced by up to 52 % compared to ENVISAT-based discharge. Finally, it is shown that localization improves results along the main tributaries.

Numerical simulations of the transport and diffusion during the 1991 Winter Validation Study along the front range in Colorado

NASA Astrophysics Data System (ADS)

Fast, J. D.; Osteen, B. L.

An important aspect of the U.S. Department of Energy's Atmospheric Studies in Complex Terrain (ASCOT) program is the development and evaluation of numerical models that predict transport and diffusion of pollutants in complex terrain. Operational mesoscale modeling of the transport of pollutants in complex terrain will become increasingly practical as computational costs decrease and additional data from high-resolution remote sensing instrumentation networks become available during the 1990s. Four-dimensional data assimilation (4DDA) techniques are receiving a great deal of attention recently not only to improve the initial conditions of mesoscale forecast models, but to create high-quality four-dimensional mesoscale analysis fields that can be used as input to air-quality models. In this study, a four-dimensional data assimilation technique based on Newtonian relaxation is incorporated into the Colorado State University (CSU) Regional Atmospheric Modeling System (RAMS) and evaluated using data taken from one experiment of the 1991 ASCOT field study along the front range of the Rockies in Colorado. The main objective of this study is to compare the observed surface concentrations with those predicted by a Lagrangian particle dispersion model and to demonstrate the effect of data assimilation on the simulated plume. In contrast to previous studies in which the smallest horizontal grid spacing was 10 km (Stauffer and Seaman, 1991) and 8 km (Yamada and Hermi, 1991), data assimilation is applied in this study to domains with a horizontal grid spacing as small as 1 km.

Allometric constraints to inversion of canopy structure from remote sensing

NASA Astrophysics Data System (ADS)

Wolf, A.; Berry, J. A.; Asner, G. P.

2008-12-01

Canopy radiative transfer models employ a large number of vegetation architectural and leaf biochemical attributes. Studies of leaf biochemistry show a wide array of chemical and spectral diversity that suggests that several leaf biochemical constituents can be independently retrieved from multi-spectral remotely sensed imagery. In contrast, attempts to exploit multi-angle imagery to retrieve canopy structure only succeed in finding two or three of the many unknown canopy arhitectural attributes. We examine a database of over 5000 destructive tree harvests from Eurasia to show that allometry - the covariation of plant form across a broad range of plant size and canopy density - restricts the architectural diversity of plant canopies into a single composite variable ranging from young canopies with many short trees with small crowns to older canopies with fewer trees and larger crowns. Moreover, these architectural attributes are closely linked to biomass via allometric constraints such as the "self-thinning law". We use the measured variance and covariance of plant canopy architecture in these stands to drive the radiative transfer model DISORD, which employs the Li-Strahler geometric optics model. This correlations introduced in the Monte Carlo study are used to determine which attributes of canopy architecture lead to important variation that can be observed by multi-angle or multi-spectral satellite observations, using the sun-view geometry characteristic of MODIS observations in different biomes located at different latitude bands. We conclude that although multi-angle/multi-spectral remote sensing is only sensitive to some of the many unknown canopy attributes that ecologists would wish to know, the strong allometric covariation between these attributes and others permits a large number of inferrences, such as forest biomass, that will be meaningful next-generation vegetation products useful for data assimilation.

On the additional information content of hyperspectral remote sensing data for estimating ecosystem carbon dioxde and energy exchange

NASA Astrophysics Data System (ADS)

Wohlfahrt, Georg; Hammerle, Albin; Tomelleri, Enrico

2015-04-01

Radiation reflected back from an ecosystem carries a spectral signature resulting from the interaction of radiation with the vegetation canopy and the underlying soil and thus allows drawing conclusions about the structure and functioning of an ecosystem. When this information is linked to a model of the leaf CO2 exchange, the ecosystem-scale CO2 exchange can be simulated. A well-known and very simplistic example for this approach is the light-use efficiency (LUE) model proposed by Monteith which links the flux of absorbed photosynthetically active radiation times a LUE parameter, both of which may be estimated based on remote sensing data, to predict the ecosystem gross photosynthesis. Here we explore the ability of a more elaborate approach by using near-surface remote sensing of hyperspectral reflected radiation, eddy covariance CO2 and energy flux measurements and a coupled radiative transfer and soil-vegetation-atmosphere-transfer (SVAT) model. Our main objective is to understand to what degree the joint assimilation of hyperspectral reflected radiation and eddy covariance flux measurements into the model helps to better constrain model parameters. To this end we use the SCOPE model, a combination of the well-known PROSAIL model and a SVAT model, and the Bayesian inversion algorithm DREAM. In order to explicitly link reflectance in the visible light and the leaf CO2 exchange, a novel parameterisation of the maximum carboxylation capacity parameter (Vcmax) on the leaf a+b chlorophyll content parameter of PROSAIL is introduced. Results are discussed with respect to the additional information content the hyperspectral data yield for simulating canopy photosynthesis.

Detailed Characterization of Nearshore Processes During NCEX

NASA Astrophysics Data System (ADS)

Holland, K.; Kaihatu, J. M.; Plant, N.

2004-12-01

Recent technology advances have allowed the coupling of remote sensing methods with advanced wave and circulation models to yield detailed characterizations of nearshore processes. This methodology was demonstrated as part of the Nearshore Canyon EXperiment (NCEX) in La Jolla, CA during Fall 2003. An array of high-resolution, color digital cameras was installed to monitor an alongshore distance of nearly 2 km out to depths of 25 m. This digital imagery was analyzed over the three-month period through an automated process to produce hourly estimates of wave period, wave direction, breaker height, shoreline position, sandbar location, and bathymetry at numerous locations during daylight hours. Interesting wave propagation patterns in the vicinity of the canyons were observed. In addition, directional wave spectra and swash / surf flow velocities were estimated using more computationally intensive methods. These measurements were used to provide forcing and boundary conditions for the Delft3D wave and circulation model, giving additional estimates of nearshore processes such as dissipation and rip currents. An optimal approach for coupling these remotely sensed observations to the numerical model was selected to yield accurate, but also timely characterizations. This involved assimilation of directional spectral estimates near the offshore boundary to mimic forcing conditions achieved under traditional approaches involving nested domains. Measurements of breaker heights and flow speeds were also used to adaptively tune model parameters to provide enhanced accuracy. Comparisons of model predictions and video observations show significant correlation. As compared to nesting within larger-scale and coarser resolution models, the advantages of providing boundary conditions data using remote sensing is much improved resolution and fidelity. For example, rip current development was both modeled and observed. These results indicate that this approach to data-model coupling is tenable and may be useful in near-real-time characterizations required by many applied scenarios.

Spatial Estimation of Evapotranspiration in an Irrigated Semi-arid Region Using LSMs Driven by Remote Sensing Data.

NASA Astrophysics Data System (ADS)

Etchanchu, J.; Delogu, E.; Saadi, S.; Chebbi, W.; Trapon, D.; Rivalland, V.; Boulet, G.; Boone, A. A.; Fanise, P.; Mougenot, B.; LE Dantec, V.

2017-12-01

Evapotranspiration and sensible-latent heat flux partition are important decision critera to manage crops, detect water stress and plan irrigation, particularly in a semi-arid context. Nowadays, remote sensing information (at medium -MODIS- and high resolution -LANDSAT, SPOT-) allows us to spatially estimate the different terms of the energy balance at daily and infra-daily time step through various approaches, either by forcing data in an energy balance model (EVASPA, Gallego-Elvira et al., 2013, and SPARSE, Boulet et al., 2015) or data assimilation in coupled water/energy balance models (SURFEX-ISBA, Noilhan et Planton, 1989). However, these different methods of flux estimations still require an evaluation through comparison to in-situ measurements and inter-comparison.The area selected for this study is the Kairouan agricultural plain, a semi-arid region in central Tunisia. Different flux datasets were acquired over two years, on an extensive rainfed oliveyard with very low vegetation cover, and on irrigated and rainfed wheat plots. In the same time, a third dataset has been acquired over a complex agricultural landscape with an eXtra-Large Aperture Scintillometer (XLAS) set-up on a 4 km transect.First, EC fluxes from towers are compared to the different model simulations at plot scale. Then a spatial comparison with retrievals of sensible and latent heat fluxes from XLAS is performed which allows to take into account the heterogeneity of the landscape (mix of wheat, irrigated oliveyards and bare soil). Effects on irrigation scenarios, through an automatic irrigation triggering method are tested and discussed. Finally, we cross-compare the different modeling approaches.We tackle the various issues: the accuracy of the measurements, the temporal frequency of remote sensing data, and the difficulty to calibrate the models.

Biodiversity and Climate Modeling Workshop Series: Identifying gaps and needs for improving large-scale biodiversity models

NASA Astrophysics Data System (ADS)

Weiskopf, S. R.; Myers, B.; Beard, T. D.; Jackson, S. T.; Tittensor, D.; Harfoot, M.; Senay, G. B.

2017-12-01

At the global scale, well-accepted global circulation models and agreed-upon scenarios for future climate from the Intergovernmental Panel on Climate Change (IPCC) are available. In contrast, biodiversity modeling at the global scale lacks analogous tools. While there is great interest in development of similar bodies and efforts for international monitoring and modelling of biodiversity at the global scale, equivalent modelling tools are in their infancy. This lack of global biodiversity models compared to the extensive array of general circulation models provides a unique opportunity to bring together climate, ecosystem, and biodiversity modeling experts to promote development of integrated approaches in modeling global biodiversity. Improved models are needed to understand how we are progressing towards the Aichi Biodiversity Targets, many of which are not on track to meet the 2020 goal, threatening global biodiversity conservation, monitoring, and sustainable use. We brought together biodiversity, climate, and remote sensing experts to try to 1) identify lessons learned from the climate community that can be used to improve global biodiversity models; 2) explore how NASA and other remote sensing products could be better integrated into global biodiversity models and 3) advance global biodiversity modeling, prediction, and forecasting to inform the Aichi Biodiversity Targets, the 2030 Sustainable Development Goals, and the Intergovernmental Platform on Biodiversity and Ecosystem Services Global Assessment of Biodiversity and Ecosystem Services. The 1st In-Person meeting focused on determining a roadmap for effective assessment of biodiversity model projections and forecasts by 2030 while integrating and assimilating remote sensing data and applying lessons learned, when appropriate, from climate modeling. Here, we present the outcomes and lessons learned from our first E-discussion and in-person meeting and discuss the next steps for future meetings.

Hyperspectral and in situ data fusion for the steering of plant production systems

NASA Astrophysics Data System (ADS)

Verstraeten, W. W.; Coppin, P.

2009-04-01

Plant production systems are governed by biotic and a-biotic factors and by management practices. Some of the relevant parameters have already been identified and incorporated as inputs into existing models for production assessment, early-warning, and process management. These parameters originate nowadays primarily from in-situ measurements and observations. Non-invasive remotely sensed data, the diagnostic tools of excellence where it concerns the interaction of solar energy with biomass, have seldom been included and if so, mostly to support yield assessment and harvest monitoring only. The availability of new-generation hyperspectral/hypertemporal signatures will greatly facilitate their integration into full-fledged plant production model either via direct use, forcing, assimilation or re-initialization strategies. The main objective of IS-HS (Integration of In Situ data and HyperSpectral remote sensing for plant production modeling) is to set up a multidisciplinary research platform to deepen our system understanding and to develop production-oriented schemes to steer capital-intensive vegetation scenarios. Real-time steering in a 10-15 year timeframe is envisaged, where current system state is monitored, and steered towards an ideal state in terms of production quantity and quality. IS-HS focuses on hyperspectral sensor design, time series analysis tools for remote sensing data of vegetation systems, on the establishment of two stream communication between satellite and ground sensors, on the development of citrus plant production systems, and on the design of in-situ data sensor networks. The general framework of this system approach will be presented. In time, this integration should allow to cross the bridge from post-harvest assessment to near real-time potential and actual yield monitoring in terms of crop.

Remote sensing of intertidal morphological change in Morecambe Bay, U.K., between 1991 and 2007

NASA Astrophysics Data System (ADS)

Mason, D. C.; Scott, T. R.; Dance, S. L.

2010-04-01

Tidal Flats are important examples of extensive areas of natural environment that remain relatively unaffected by man. Monitoring of tidal flats is required for a variety of purposes. Remote sensing has become an established technique for the measurement of topography over tidal flats. A further requirement is to measure topographic changes in order to measure sediment budgets. To date there have been few attempts to make quantitative estimates of morphological change over tidal flat areas. This paper illustrates the use of remote sensing to measure quantitative and qualitative changes in the tidal flats of Morecambe Bay during the relatively long period 1991-2007. An understanding of the patterns of sediment transport within the Bay is of considerable interest for coastal management and defence purposes. Tidal asymmetry is considered to be the dominant cause of morphological change in the Bay, with the higher currents associated with the flood tide being the main agency moulding the channel system. Quantitative changes were measured by comparing a Digital Elevation Model (DEM) of the intertidal zone formed using the waterline technique applied to satellite Synthetic Aperture Radar (SAR) images from 1991-1994, to a second DEM constructed from airborne laser altimetry data acquired in 2005. Qualitative changes were studied using additional SAR images acquired since 2003. A significant movement of sediment from below Mean Sea Level (MSL) to above MSL was detected by comparing the two Digital Elevation Models, though the proportion of this change that could be ascribed to seasonal effects was not clear. Between 1991 and 2004 there was a migration of the Ulverston channel of the river Leven north-east by about 5 km, followed by the development of a straighter channel to the west, leaving the previous channel decoupled from the river. This is thought to be due to independent tidal and fluvial forcing mechanisms acting on the channel. The results demonstrate the effectiveness of remote sensing for measurement of long-term morphological change in tidal flat areas. An alternative use of waterlines as partial bathymetry for assimilation into a morphodynamic model of the coastal zone is also discussed.

A high throughput geocomputing system for remote sensing quantitative retrieval and a case study

NASA Astrophysics Data System (ADS)

Xue, Yong; Chen, Ziqiang; Xu, Hui; Ai, Jianwen; Jiang, Shuzheng; Li, Yingjie; Wang, Ying; Guang, Jie; Mei, Linlu; Jiao, Xijuan; He, Xingwei; Hou, Tingting

2011-12-01

The quality and accuracy of remote sensing instruments have been improved significantly, however, rapid processing of large-scale remote sensing data becomes the bottleneck for remote sensing quantitative retrieval applications. The remote sensing quantitative retrieval is a data-intensive computation application, which is one of the research issues of high throughput computation. The remote sensing quantitative retrieval Grid workflow is a high-level core component of remote sensing Grid, which is used to support the modeling, reconstruction and implementation of large-scale complex applications of remote sensing science. In this paper, we intend to study middleware components of the remote sensing Grid - the dynamic Grid workflow based on the remote sensing quantitative retrieval application on Grid platform. We designed a novel architecture for the remote sensing Grid workflow. According to this architecture, we constructed the Remote Sensing Information Service Grid Node (RSSN) with Condor. We developed a graphic user interface (GUI) tools to compose remote sensing processing Grid workflows, and took the aerosol optical depth (AOD) retrieval as an example. The case study showed that significant improvement in the system performance could be achieved with this implementation. The results also give a perspective on the potential of applying Grid workflow practices to remote sensing quantitative retrieval problems using commodity class PCs.

Radar Altimetry for Hydrological Modeling and Monitoring in the Zambezi River Basin

NASA Astrophysics Data System (ADS)

Michailovsky, C. I.; Berry, P. A.; Smith, R. G.; Bauer-Gottwein, P.

2011-12-01

Hydrological model forecasts are subject to large uncertainties stemming from uncertain input data, model structure, parameterization and lack of sufficient calibration/validation data. For real-time or near-real-time applications data assimilation techniques such as the Ensemble Kalman Filter (EnKF) can be used to reduce forecast uncertainty by updating model states as new data becomes available. The use of remote sensing data is attractive for such applications as it provides wide geographical coverage and continuous time-series without the typically long delays that exist in obtaining in-situ data. River discharge is one of the main hydrological variables of interest, and while it cannot currently be directly measured remotely, water levels in rivers can be obtained from satellite based radar altimetry and converted to discharge through rating curves. This study aims to give a realistic assessment of the improvements that can be derived from the use of satellite radar altimetry measurements from the Envisat mission for discharge monitoring and modeling on the basin scale for the Zambezi River. The altimetry data used is the Radar AlTimetry (RAT) product developed at the Earth and Planetary Remote Sensing Laboratory at the De Montfort University. The first step in analyzing the data is the determination of potential altimetry targets which are the locations at which the Envisat orbit and the river network cross in order to select data points corresponding to surface water. The quality of the water level time-series is then analyzed for all targets and the exploitable targets identified. Rating curves are derived from in-situ or remotely-sensed data depending on data-availability at the various locations and discharge time-series are established. A Monte Carlo analysis is carried out to assess the uncertainties on the computed discharge. It was found that having a single cross-section and associated discharge measurement at one point in time significantly reduces discharge uncertainty. To assess improvements in model predictions, a model of the Zambezi River basin based on remote sensing data is set up with the Soil and Water Assessment Tool and calibrated with available in-situ data. The discharge data from altimetry is then used in an EnKF framework to update discharge in the model as it runs. The method showed improvements in prediction uncertainties for short lead times.

NASA Supercomputer Improves Prospects for Ocean Climate Research

NASA Technical Reports Server (NTRS)

Menemenlis, D.; Hill, C.; Adcroft, A.; Campin, J. -M.; Cheng, B.; Ciotti, B.; Fukumori, I.; Heimbach, P.; Henze, C.; Kohl, A.;

Applications of Remote Sensing to Emergency Management.

DTIC Science & Technology

1980-02-15

Contents: Foundations of Remote Sensing : Data Acquisition and Interpretation; Availability of Remote Sensing Technology for Disaster Response...Imaging Systems, Current and Near Future Satellite and Aircraft Remote Sensing Systems; Utilization of Remote Sensing in Disaster Response: Categories of...Disasters, Phases of Monitoring Activities; Recommendations for Utilization of Remote Sensing Technology in Disaster Response; Selected Reading List.

Impact of Atmospheric Infrared Sounder (AIRS) Thermodynamic Profiles on Regional Weather Forecasting

NASA Technical Reports Server (NTRS)

Chou, Shih-Hung; Zavodsky, Bradley T.; Jedlovee, Gary J.

2010-01-01

In data sparse regions, remotely-sensed observations can be used to improve analyses and lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles with accuracy comparable to that of radiosondes. The purpose of this paper is to describe a procedure to assimilate AIRS thermodynamic profile data into a regional configuration of the Advanced Research Weather Research and Forecasting (WRF-ARW) model using its three-dimension variational (3DVAR) analysis component (WRF-Var). Quality indicators are used to select only the highest quality temperature and moisture profiles for assimilation in both clear and partly cloudy regions. Separate error characteristics for land and water profiles are also used in the assimilation process. Assimilation results indicate that AIRS profiles produce an analysis closer to in situ observations than the background field. Forecasts from a 37-day case study period in the winter of 2007 show that AIRS profile data can lead to improvements in 6-h cumulative precipitation forecasts due to instability added in the forecast soundings by the AIRS profiles. Additionally, in a convective heavy rainfall event from February 2007, assimilation of AIRS profiles produces a more unstable boundary layer resulting in enhanced updrafts in the model. These updrafts produce a squall line and precipitation totals that more closely reflect ground-based observations than a no AIRS control forecast. The location of available high-quality AIRS profiles ahead of approaching storm systems is found to be of paramount importance to the amount of impact the observations will have on the resulting forecasts.

Assimilation of flood extent data with 2D flood inundation models for localised intense rainfall events

NASA Astrophysics Data System (ADS)

Neal, J. C.; Wood, M.; Bermúdez, M.; Hostache, R.; Freer, J. E.; Bates, P. D.; Coxon, G.

2017-12-01

Remote sensing of flood inundation extent has long been a potential source of data for constraining and correcting simulations of floodplain inundation. Hydrodynamic models and the computing resources to run them have developed to the extent that simulation of flood inundation in two-dimensional space is now feasible over large river basins in near real-time. However, despite substantial evidence that there is useful information content within inundation extent data, even from low resolution SAR such as that gathered by Envisat ASAR in wide swath mode, making use of the information in a data assimilation system has proved difficult. He we review recent applications of the Ensemble Kalman Filter (EnKF) and Particle Filter for assimilating SAR data, with a focus on the River Severn UK and compare these with complementary research that has looked at the internal error sources and boundary condition errors using detailed terrestrial data that is not available in most locations. Previous applications of the EnKF to this reach have focused on upstream boundary conditions as the source of flow error, however this description of errors was too simplistic for the simulation of summer flood events where localised intense rainfall can be substantial. Therefore, we evaluate the introduction of uncertain lateral inflows to the ensemble. A further limitation of the existing EnKF based methods is the need to convert flood extent to water surface elevations by intersecting the shoreline location with a high quality digital elevation model (e.g. LiDAR). To simplify this data processing step, we evaluate a method to directly assimilate inundation extent as a EnKF model state rather than assimilating water heights, potentially allowing the scheme to be used where high-quality terrain data are sparse.

Constraining a land-surface model with multiple observations by application of the MPI-Carbon Cycle Data Assimilation System V1.0

NASA Astrophysics Data System (ADS)

Schürmann, Gregor J.; Kaminski, Thomas; Köstler, Christoph; Carvalhais, Nuno; Voßbeck, Michael; Kattge, Jens; Giering, Ralf; Rödenbeck, Christian; Heimann, Martin; Zaehle, Sönke

2016-09-01

We describe the Max Planck Institute Carbon Cycle Data Assimilation System (MPI-CCDAS) built around the tangent-linear version of the JSBACH land-surface scheme, which is part of the MPI-Earth System Model v1. The simulated phenology and net land carbon balance were constrained by globally distributed observations of the fraction of absorbed photosynthetically active radiation (FAPAR, using the TIP-FAPAR product) and atmospheric CO2 at a global set of monitoring stations for the years 2005 to 2009. When constrained by FAPAR observations alone, the system successfully, and computationally efficiently, improved simulated growing-season average FAPAR, as well as its seasonality in the northern extra-tropics. When constrained by atmospheric CO2 observations alone, global net and gross carbon fluxes were improved, despite a tendency of the system to underestimate tropical productivity. Assimilating both data streams jointly allowed the MPI-CCDAS to match both observations (TIP-FAPAR and atmospheric CO2) equally well as the single data stream assimilation cases, thereby increasing the overall appropriateness of the simulated biosphere dynamics and underlying parameter values. Our study thus demonstrates the value of multiple-data-stream assimilation for the simulation of terrestrial biosphere dynamics. It further highlights the potential role of remote sensing data, here the TIP-FAPAR product, in stabilising the strongly underdetermined atmospheric inversion problem posed by atmospheric transport and CO2 observations alone. Notwithstanding these advances, the constraint of the observations on regional gross and net CO2 flux patterns on the MPI-CCDAS is limited through the coarse-scale parametrisation of the biosphere model. We expect improvement through a refined initialisation strategy and inclusion of further biosphere observations as constraints.

Remote Sensing and Capacity Building to Improve Food Security

NASA Astrophysics Data System (ADS)

Husak, G. J.; Funk, C. C.; Verdin, J. P.; Rowland, J.; Budde, M. E.

2012-12-01

The Famine Early Warning Systems Network (FEWS NET) is a U.S. Agency for International Development (USAID) supported project designed to monitor and anticipate food insecurity in the developing world, primarily Africa, Central America, the Caribbean and Central Asia. This is done through a network of partners involving U.S. government agencies, universities, country representatives, and partner institutions. This presentation will focus on the remotely sensed data used in FEWS NET activities and capacity building efforts designed to expand and enhance the use of FEWS NET tools and techniques. Remotely sensed data are of particular value in the developing world, where ground data networks and data reporting are limited. FEWS NET uses satellite based rainfall and vegetation greenness measures to monitor and assess food production conditions. Satellite rainfall estimates also drive crop models which are used in determining yield potential. Recent FEWS NET products also include estimates of actual evapotranspiration. Efforts are currently underway to assimilate these products into a single tool which would indicate areas experiencing abnormal conditions with implications for food production. FEWS NET is also involved in a number of capacity building activities. Two primary examples are the development of software and training of institutional partners in basic GIS and remote sensing. Software designed to incorporate rainfall station data with existing satellite-derived rainfall estimates gives users the ability to enhance satellite rainfall estimates or long-term means, resulting in gridded fields of rainfall that better reflect ground conditions. Further, this software includes a crop water balance model driven by the improved rainfall estimates. Finally, crop parameters, such as the planting date or length of growing period, can be adjusted by users to tailor the crop model to actual conditions. Training workshops in the use of this software, as well as basic GIS and remote sensing tools, are routinely conducted by FEWS NET representatives at host country meteorological and agricultural services. These institutions are then able to produce information that can more accurately inform food security decision making. Informed decision making reduces the risk associated with a given hazard. In the case of FEWS NET, this involves identification of shocks to food availability, allowing for the pre-positioning of aid to be available when a hazard strikes. Developing tools to incorporate better information in food production estimates and working closely with local staff trained in state-of-the-practice techniques results in a more informed decision making process, reducing the impacts of food security hazards.

The remote sensing image segmentation mean shift algorithm parallel processing based on MapReduce

NASA Astrophysics Data System (ADS)

Chen, Xi; Zhou, Liqing

2015-12-01

With the development of satellite remote sensing technology and the remote sensing image data, traditional remote sensing image segmentation technology cannot meet the massive remote sensing image processing and storage requirements. This article put cloud computing and parallel computing technology in remote sensing image segmentation process, and build a cheap and efficient computer cluster system that uses parallel processing to achieve MeanShift algorithm of remote sensing image segmentation based on the MapReduce model, not only to ensure the quality of remote sensing image segmentation, improved split speed, and better meet the real-time requirements. The remote sensing image segmentation MeanShift algorithm parallel processing algorithm based on MapReduce shows certain significance and a realization of value.

REMOTE SENSING TECHNOLOGIES APPLICATIONS RESEARCH

EPA Science Inventory

Remote sensing technologies applications research supports the ORD Landscape Sciences Program (LSP) in two separate areas: operational remote sensing, and remote sensing research and development. Operational remote sensing is provided to the LSP through the use of current and t...

Drought Prediction for Socio-Cultural Stability Project

NASA Technical Reports Server (NTRS)

Peters-Lidard, Christa; Eylander, John B.; Koster, Randall; Narapusetty, Balachandrudu; Kumar, Sujay; Rodell, Matt; Bolten, John; Mocko, David; Walker, Gregory; Arsenault, Kristi;

Enhanced Soil Moisture Initialization Using Blended Soil Moisture Product and Regional Optimization of LSM-RTM Coupled Land Data Assimilation System.

NASA Astrophysics Data System (ADS)

Nair, A. S.; Indu, J.

2017-12-01

Prediction of soil moisture dynamics is high priority research challenge because of the complex land-atmosphere interaction processes. Soil moisture (SM) plays a decisive role in governing water and energy balance of the terrestrial system. An accurate SM estimate is imperative for hydrological and weather prediction models. Though SM estimates are available from microwave remote sensing and land surface model (LSM) simulations, it is affected by uncertainties from several sources during estimation. Past studies have generally focused on land data assimilation (DA) for improving LSM predictions by assimilating soil moisture from single satellite sensor. This approach is limited by the large time gap between two consequent soil moisture observations due to satellite repeat cycle of more than three days at the equator. To overcome this, in the present study, we have performed DA using ensemble products from the soil moisture operational product system (SMOPS) blended soil moisture retrievals from different satellite sensors into Noah LSM. Before the assimilation period, the Noah LSM is initialized by cycling through seven multiple loops from 2008 to 2010 forcing with Global data assimilation system (GDAS) data over the Indian subcontinent. We assimilated SMOPS into Noah LSM for a period of two years from 2010 to 2011 using Ensemble Kalman Filter within NASA's land information system (LIS) framework. Results show that DA has improved Noah LSM prediction with a high correlation of 0.96 and low root mean square difference of 0.0303 m3/m3 (figure 1a). Further, this study has also investigated the notion of assimilating microwave brightness temperature (Tb) as a proxy for SM estimates owing to the close proximity of Tb and SM. Preliminary sensitivity analysis show a strong need for regional parameterization of radiative transfer models (RTMs) to improve Tb simulation. Towards this goal, we have optimized the forward RTM using swarm optimization technique for direct Tb assimilation. The results indicate an improvement in Tb simulations based on the multi polarization difference index approach with a correlation of 0.81 (figure 1b (e)) and bias of < 5 K with respect to the SMOS Tb.

Aerosol Observability and Predictability: From Research to Operations for Chemical Weather Forecasting. Lagrangian Displacement Ensembles for Aerosol Data Assimilation

NASA Technical Reports Server (NTRS)

da Silva, Arlindo

2010-01-01

A challenge common to many constituent data assimilation applications is the fact that one observes a much smaller fraction of the phase space that one wishes to estimate. For example, remotely sensed estimates of the column average concentrations are available, while one is faced with the problem of estimating 3D concentrations for initializing a prognostic model. This problem is exacerbated in the case of aerosols because the observable Aerosol Optical Depth (AOD) is not only a column integrated quantity, but it also sums over a large number of species (dust, sea-salt, carbonaceous and sulfate aerosols. An aerosol transport model when driven by high-resolution, state-of-the-art analysis of meteorological fields and realistic emissions can produce skillful forecasts even when no aerosol data is assimilated. The main task of aerosol data assimilation is to address the bias arising from inaccurate emissions, and Lagrangian misplacement of plumes induced by errors in the driving meteorological fields. As long as one decouples the meteorological and aerosol assimilation as we do here, the classic baroclinic growth of error is no longer the main order of business. We will describe an aerosol data assimilation scheme in which the analysis update step is conducted in observation space, using an adaptive maximum-likelihood scheme for estimating background errors in AOD space. This scheme includes e explicit sequential bias estimation as in Dee and da Silva. Unlikely existing aerosol data assimilation schemes we do not obtain analysis increments of the 3D concentrations by scaling the background profiles. Instead we explore the Lagrangian characteristics of the problem for generating local displacement ensembles. These high-resolution state-dependent ensembles are then used to parameterize the background errors and generate 3D aerosol increments. The algorithm has computational complexity running at a resolution of 1/4 degree, globally. We will present the result of assimilating AOD retrievals from MODIS (on both Aqua and TERRA satellites) from AERONET for validation. The impact on the GEOS-5 Aerosol Forecasting will be fully documented.

Evaluating the Capacity of Global CO2 Flux and Atmospheric Transport Models to Incorporate New Satellite Observations

NASA Technical Reports Server (NTRS)

Kawa, S. R.; Collatz, G. J.; Erickson, D. J.; Denning, A. S.; Wofsy, S. C.; Andrews, A. E.

2007-01-01

As we enter the new era of satellite remote sensing for CO2 and other carbon cyclerelated quantities, advanced modeling and analysis capabilities are required to fully capitalize on the new observations. Model estimates of CO2 surface flux and atmospheric transport are required for initial constraints on inverse analyses, to connect atmospheric observations to the location of surface sources and sinks, and ultimately for future projections of carbon-climate interactions. For application to current, planned, and future remotely sensed CO2 data, it is desirable that these models are accurate and unbiased at time scales from less than daily to multi-annual and at spatial scales from several kilometers or finer to global. Here we focus on simulated CO2 fluxes from terrestrial vegetation and atmospheric transport mutually constrained by analyzed meteorological fields from the Goddard Modeling and Assimilation Office for the period 1998 through 2006. Use of assimilated meteorological data enables direct model comparison to observations across a wide range of scales of variability. The biospheric fluxes are produced by the CASA model at lxi degrees on a monthly mean basis, modulated hourly with analyzed temperature and sunlight. Both physiological and biomass burning fluxes are derived using satellite observations of vegetation, burned area (as in GFED-2), and analyzed meteorology. For the purposes of comparison to CO2 data, fossil fuel and ocean fluxes are also included in the transport simulations. In this presentation we evaluate the model's ability to simulate CO2 flux and mixing ratio variability in comparison to in situ observations at sites in Northern mid latitudes and the continental tropics. The influence of key process representations is inferred. We find that the model can resolve much of the hourly to synoptic variability in the observations, although there are limits imposed by vertical resolution of boundary layer processes. The seasonal cycle and its interannual variations generally respond adequately, but discrepancies in the tropics suggest the need for a refinement of the soil moisture dependence of the respiration flux in CASA. Examples and inferences for interpretation of satellite data will be discussed. In general, the fidelity of these simulations leads us to anticipate incorporation of real-time, highly resolved remote sensing and other observations into quantitative analyses that will reduce uncertainty in the terrestrial CO2 sink and revolutionize our understanding of the key processes controlling atmospheric CO2 and its evolution with time.

Estimation of Carbon Budgets for Croplands by Combining High Resolution Remote Sensing Data with a Crop Model and Validation Ground Data

NASA Astrophysics Data System (ADS)

Mangiarotti, S.; Veloso, A.; Ceschia, E.; Tallec, T.; Dejoux, J. F.

2015-12-01

Croplands occupy large areas of Earth's land surface playing a key role in the terrestrial carbon cycle. Hence, it is essential to quantify and analyze the carbon fluxes from those agro-ecosystems, since they contribute to climate change and are impacted by the environmental conditions. In this study we propose a regional modeling approach that combines high spatial and temporal resolutions (HSTR) optical remote sensing data with a crop model and a large set of in-situ measurements for model calibration and validation. The study area is located in southwest France and the model that we evaluate, called SAFY-CO2, is a semi-empirical one based on the Monteith's light-use efficiency theory and adapted for simulating the components of the net ecosystem CO2 fluxes (NEE) and of the annual net ecosystem carbon budgets (NECB) at a daily time step. The approach is based on the assimilation of satellite-derived green area index (GAI) maps for calibrating a number of the SAFY-CO2 parameters linked to crop phenology. HSTR data from the Formosat-2 and SPOT satellites were used to produce the GAI maps. The experimental data set includes eddy covariance measurements of net CO2 fluxes from two experimental sites and partitioned into gross primary production (GPP) and ecosystem respiration (Reco). It also includes measurements of GAI, biomass and yield between 2005 and 2011, focusing on the winter wheat crop. The results showed that the SAFY-CO2 model correctly reproduced the biomass production, its dynamic and the yield (relative errors about 24%) in contrasted climatic, environmental and management conditions. The net CO2 flux components estimated with the model were overall in agreement with the ground data, presenting good correlations (R² about 0.93 for GPP, 0.77 for Reco and 0.86 for NEE). The evaluation of the modelled NECB for the different site-years highlighted the importance of having accurate estimates of each component of the NECB. Future works aim at considering systematically post-harvest events (such as re-growths, weeds and intercrops) on NEE assessment and at assimilating radar remote sensing data for estimating GAI and biomass more accurately. This approach is currently being extended to summer crops and it could be applied to larger scales thanks to the recent satellite missions (Landsat-8, Sentinel-1 and 2…).

Remote sensing-based estimation of annual soil respiration at two contrasting forest sites

NASA Astrophysics Data System (ADS)

Huang, Ni; Gu, Lianhong; Black, T. Andrew; Wang, Li; Niu, Zheng

2015-11-01

Soil respiration (Rs), an important component of the global carbon cycle, can be estimated using remotely sensed data, but the accuracy of this technique has not been thoroughly investigated. In this study, we proposed a methodology for the remote estimation of annual Rs at two contrasting FLUXNET forest sites (a deciduous broadleaf forest and an evergreen needleleaf forest). A version of the Akaike's information criterion was used to select the best model from a range of models for annual Rs estimation based on the remotely sensed data products from the Moderate Resolution Imaging Spectroradiometer and root-zone soil moisture product derived from assimilation of the NASA Advanced Microwave Scanning Radiometer soil moisture products and a two-layer Palmer water balance model. We found that the Arrhenius-type function based on nighttime land surface temperature (LST-night) was the best model by comprehensively considering the model explanatory power and model complexity at the Missouri Ozark and BC-Campbell River 1949 Douglas-fir sites. In addition, a multicollinearity problem among LST-night, root-zone soil moisture, and plant photosynthesis factor was effectively avoided by selecting the LST-night-driven model. Cross validation showed that temporal variation in Rs was captured by the LST-night-driven model with a mean absolute error below 1 µmol CO2 m-2 s-1 at both forest sites. An obvious overestimation that occurred in 2005 and 2007 at the Missouri Ozark site reduced the evaluation accuracy of cross validation because of summer drought. However, no significant difference was found between the Arrhenius-type function driven by LST-night and the function considering LST-night and root-zone soil moisture. This finding indicated that the contribution of soil moisture to Rs was relatively small at our multiyear data set. To predict intersite Rs, maximum leaf area index (LAImax) was used as an upscaling factor to calibrate the site-specific reference respiration rates. Independent validation demonstrated that the model incorporating LST-night and LAImax efficiently predicted the spatial and temporal variabilities of Rs. Based on the Arrhenius-type function using LST-night as an input parameter, the rates of annual C release from Rs were 894-1027 g C m-2 yr-1 at the BC-Campbell River 1949 Douglas-fir site and 818-943 g C m-2 yr-1 at the Missouri Ozark site. The ratio between annual Rs estimates based on remotely sensed data and the total annual ecosystem respiration from eddy covariance measurements fell within the range reported in previous studies. Our results demonstrated that estimating annual Rs based on remote sensing data products was possible at deciduous and evergreen forest sites.

Ensemble Data Assimilation of Photovoltaic Power Information in the Convection-permitting High-Resolution Model COSMO-DE

NASA Astrophysics Data System (ADS)

Declair, Stefan; Saint-Drenan, Yves-Marie; Potthast, Roland

2017-04-01

Determining the amount of weather dependent renewable energy is a demanding task for transmission system operators (TSOs) and wind and photovoltaic (PV) prediction errors require the use of reserve power, which generate costs and can - in extreme cases - endanger the security of supply. In the project EWeLiNE funded by the German government, the German Weather Service and the Fraunhofer Institute on Wind Energy and Energy System Technology develop innovative weather- and power forecasting models and tools for grid integration of weather dependent renewable energy. The key part in energy prediction process chains is the numerical weather prediction (NWP) system. Irradiation forecasts from NWP systems are however subject to several sources of error. For PV power prediction, weaknesses of the NWP model to correctly forecast i.e. low stratus, absorption of condensed water or aerosol optical depths are the main sources of errors. Inaccurate radiation schemes (i.e. the two-stream parametrization) are also known as a deficit of NWP systems with regard to irradiation forecast. To mitigate errors like these, latest observations can be used in a pre-processing technique called data assimilation (DA). In DA, not only the initial fields are provided, but the model is also synchronized with reality - the observations - and hence forecast errors are reduced. Besides conventional observation networks like radiosondes, synoptic observations or air reports of wind, pressure and humidity, the number of observations measuring meteorological information indirectly by means of remote sensing such as satellite radiances, radar reflectivities or GPS slant delays strongly increases. Numerous PV plants installed in Germany potentially represent a dense meteorological network assessing irradiation through their power measurements. Forecast accuracy may thus be enhanced by extending the observations in the assimilation by this new source of information. PV power plants can provide information on clouds, aerosol optical depth or low stratus in terms of remote sensing: the power output is strongly dependent on perturbations along the slant between sun position and PV panel. Since these data are not limited to the vertical column above or below the detector, it may thus complement satellite data and compensate weaknesses in the radiation scheme. In this contribution, the used DA technique (Local Ensemble Transform Kalman Filter, LETKF) is shortly sketched. Furthermore, the computation of the model power equivalents is described and first results are presented and discussed.

Integrating remotely sensed land cover observations and a biogeochemical model for estimating forest ecosystem carbon dynamics

USGS Publications Warehouse

Liu, J.; Liu, S.; Loveland, Thomas R.; Tieszen, L.L.

2008-01-01

Land cover change is one of the key driving forces for ecosystem carbon (C) dynamics. We present an approach for using sequential remotely sensed land cover observations and a biogeochemical model to estimate contemporary and future ecosystem carbon trends. We applied the General Ensemble Biogeochemical Modelling System (GEMS) for the Laurentian Plains and Hills ecoregion in the northeastern United States for the period of 1975-2025. The land cover changes, especially forest stand-replacing events, were detected on 30 randomly located 10-km by 10-km sample blocks, and were assimilated by GEMS for biogeochemical simulations. In GEMS, each unique combination of major controlling variables (including land cover change history) forms a geo-referenced simulation unit. For a forest simulation unit, a Monte Carlo process is used to determine forest type, forest age, forest biomass, and soil C, based on the Forest Inventory and Analysis (FIA) data and the U.S. General Soil Map (STATSGO) data. Ensemble simulations are performed for each simulation unit to incorporate input data uncertainty. Results show that on average forests of the Laurentian Plains and Hills ecoregion have been sequestrating 4.2 Tg C (1 teragram = 1012 gram) per year, including 1.9 Tg C removed from the ecosystem as the consequences of land cover change. ?? 2008 Elsevier B.V.

Real time measurement of transient event emissions of air toxics by tomographic remote sensing in tandem with mobile monitoring

NASA Astrophysics Data System (ADS)

Olaguer, Eduardo P.; Stutz, Jochen; Erickson, Matthew H.; Hurlock, Stephen C.; Cheung, Ross; Tsai, Catalina; Colosimo, Santo F.; Festa, James; Wijesinghe, Asanga; Neish, Bradley S.

2017-02-01

During the Benzene and other Toxics Exposure (BEE-TEX) study, a remote sensing network based on long path Differential Optical Absorption Spectroscopy (DOAS) was set up in the Manchester neighborhood beside the Ship Channel of Houston, Texas in order to perform Computer Aided Tomography (CAT) scans of hazardous air pollutants. On 18-19 February 2015, the CAT scan network detected large nocturnal plumes of toluene and xylenes most likely associated with railcar loading and unloading operations at Ship Channel petrochemical facilities. The presence of such plumes during railcar operations was confirmed by a mobile laboratory equipped with a Proton Transfer Reaction-Mass Spectrometer (PTR-MS), which measured transient peaks of toluene and C2-benzenes of 50 ppb and 57 ppb respectively around 4 a.m. LST on 19 February 2015. Plume reconstruction and source attribution were performed using the 4D variational data assimilation technique and a 3D micro-scale forward and adjoint air quality model based on both tomographic and PTR-MS data. Inverse model estimates of fugitive emissions associated with railcar transfer emissions ranged from 2.0 to 8.2 kg/hr for toluene and from 2.2 to 3.5 kg/hr for xylenes in the early morning of 19 February 2015.

Evaluation of alternative model-data fusion approaches in water balance estimation across Australia

NASA Astrophysics Data System (ADS)

van Dijk, A. I. J. M.; Renzullo, L. J.

2009-04-01

Australia's national agencies are developing a continental modelling system to provide a range of water information services. It will include rolling water balance estimation to underpin national water accounts, water resources assessments that interpret current water resources availability and trends in a historical context, and water resources predictions coupled to climate and weather forecasting. The nation-wide coverage, currency, accuracy, and consistency required means that remote sensing will need to play an important role along with in-situ observations. Different approaches to blending models and observations can be considered. Integration of on-ground and remote sensing data into land surface models in atmospheric applications often involves state updating through model-data assimilation techniques. By comparison, retrospective water balance estimation and hydrological scenario modelling to date has mostly relied on static parameter fitting against observations and has made little use of earth observation. The model-data fusion approach most appropriate for a continental water balance estimation system will need to consider the trade-off between computational overhead and the accuracy gains achieved when using more sophisticated synthesis techniques and additional observations. This trade-off was investigated using a landscape hydrological model and satellite-based estimates of soil moisture and vegetation properties for aseveral gauged test catchments in southeast Australia.

Regional Scale Meteorological Analysis and Prediction Using GPS Occultation and EOS Data

NASA Technical Reports Server (NTRS)

Bromwich, David H.; Shum, C. K.; Zhao, Changyin; Kuo, Bill; Rocken, Chris

2004-01-01

The main objective of the research under this award is to improve regional meteorological analysis and prediction for traditionally data limited regions, particularly over the Southern Ocean and Antarctica, using the remote sensing observations from current and upcoming GPS radio occultation missions and the EOS instrument suite. The major components of this project are: 1.Develop and improve the methods for retrieving temperature, moisture, and pressure profiles from GPS radio occultation data and EOS radiometer data. 2. Develop and improve a regional scale data assimilation system (MM5 4DVAR). 3. Perform case studies involving data analysis and numerical modeling to investigate the impact of different data for regional meteorological analysis and the importance of data assimilation for regional meteorological simulation over the Antarctic region. 4. Apply the findings and improvements from the above studies to weather forecasting experiments. 5. In the third year of the award we made significant progress toward the remaining goals of the project. The work included carefully evaluating the performance of an atmospheric mesoscale model, the Polar MM5 in Antarctic applications and improving the upper boundary condition.

Tunnel-Site Selection by Remote Sensing Techniques

DTIC Science & Technology

A study of the role of remote sensing for geologic reconnaissance for tunnel-site selection was commenced. For this study, remote sensing was defined...conventional remote sensing . Future research directions are suggested, and the extension of remote sensing to include airborne passive microwave

System and method for evaluating wind flow fields using remote sensing devices

DOEpatents

Schroeder, John; Hirth, Brian; Guynes, Jerry

2016-12-13

The present invention provides a system and method for obtaining data to determine one or more characteristics of a wind field using a first remote sensing device and a second remote sensing device. Coordinated data is collected from the first and second remote sensing devices and analyzed to determine the one or more characteristics of the wind field. The first remote sensing device is positioned to have a portion of the wind field within a first scanning sector of the first remote sensing device. The second remote sensing device is positioned to have the portion of the wind field disposed within a second scanning sector of the second remote sensing device.

Spatial Surface PM2.5 Concentration Estimates for Wildfire Smoke Plumes in the Western U.S. Using Satellite Retrievals and Data Assimilation Techniques

NASA Astrophysics Data System (ADS)

Loria Salazar, S. M.; Holmes, H.

2015-12-01

Health effects studies of aerosol pollution have been extended spatially using data assimilation techniques that combine surface PM2.5 concentrations and Aerosol Optical Depth (AOD) from satellite retrievals. While most of these models were developed for the dark-vegetated eastern U.S. they are being used in the semi-arid western U.S. to remotely sense atmospheric aerosol concentrations. These models are helpful to understand the spatial variability of surface PM2.5concentrations in the western U.S. because of the sparse network of surface monitoring stations. However, the models developed for the eastern U.S. are not robust in the western U.S. due to different aerosol formation mechanisms, transport phenomena, and optical properties. This region is a challenge because of complex terrain, anthropogenic and biogenic emissions, secondary organic aerosol formation, smoke from wildfires, and low background aerosol concentrations. This research concentrates on the use and evaluation of satellite remote sensing to estimate surface PM2.5 concentrations from AOD satellite retrievals over California and Nevada during the summer months of 2012 and 2013. The aim of this investigation is to incorporate a spatial statistical model that uses AOD from AERONET as well as MODIS, surface PM2.5 concentrations, and land-use regression to characterize spatial surface PM2.5 concentrations. The land use regression model uses traditional inputs (e.g. meteorology, population density, terrain) and non-traditional variables (e.g. FIre Inventory from NCAR (FINN) emissions and MODIS albedo product) to account for variability related to smoke plume trajectories and land use. The results will be used in a spatially resolved health study to determine the association between wildfire smoke exposure and cardiorespiratory health endpoints. This relationship can be used with future projections of wildfire emissions related to climate change and droughts to quantify the expected health impact.

Challenges in coupling LTER with environmental assessments: An insight from potential and reality of the Chinese Ecological Research Network in servicing environment assessments.

PubMed

Xia, Shaoxia; Liu, Yu; Yu, Xiubo; Fu, Bojie

2018-08-15

Environmental assessments estimate, evaluate and predict the consequences of natural processes and human activities on the environment. Long-term ecosystem observation and research networks (LTERs) are potentially valuable infrastructure to support environmental assessments. However, very few environmental assessments have successfully incorporated them. In this study, we try to reveal the current status of coupling LTERs with environmental assessments and look at the challenges involved in improving this coupling through exploring the role that Chinese Ecological Research Network (CERN), the LTER of China, currently plays in regional environment assessments. A review of official protocols and standards, regional assessments and CERN researches related to ecosystems and environment shows that there is great potential for coupling CERN with environment assessments. However in practice, CERN does not currently play the expected role. Remote sensing and irregular inventory data are still the main data sources currently used in regional assessments. Several causes led to the present situation: (1) insufficient cross-site research and failure to scale up site-level variables to the regional scale; (2) data barriers resulting from incompatible protocols and low data usability due to lack of data assimilation and scaling; and (3) absence of indicators relevant to human activities in existing monitoring protocols. For these reasons, enhancing cross-site monitoring and research, data assimilation and scaling up are critical steps required to improve coupling of LTER with environmental assessments. Site-focused long-term monitoring should be combined with wide-scale ground surveys and remote sensing to establish an effective connection between different environmental monitoring platforms for regional assessments. It is also necessary to revise the current monitoring protocols to include human activities and their impacts on the ecosystem, or change the LTERs into Long-Term Socio-Ecological Research (LTSER) networks. Copyright © 2018 Elsevier B.V. All rights reserved.

Project 5322 Mid-Term Report: Key Eco-Hydrological Parameters Retrieval And Land Data Assimilation System Development In A Typical Inland River Basin Of Chinas Arid Region

NASA Astrophysics Data System (ADS)

Faivre, R.; Colin, J.; Menenti, M.; Lindenbergh, R.; Van Den Bergh, L.; Yu, H.; Jia, L.; Xin, L.

2010-10-01

Improving the understanding and the monitoring of high elevation regions hydrology is of major relevance from both societal and environmental points of view for many Asian countries, in particular in terms of flood and drought, but also in terms of food security in a chang- ing environment. Satellite and airborne remote sensing technologies are of utmost for such a challenge. Exist- ing imaging spectro-radiometers, radars, microwave ra- diometers and backscatter LIDAR provide a very com- prehensive suite of measurements over a wide rage of wavelengths, time frequencies and spatial resolu- tions. It is however needed to devise new algorithms to convert these radiometric measurements into useful eco-hydrological quantitative parameters for hydrologi- cal modeling and water management. The DRAGON II project entitled Key Eco-Hydrological Parameters Re- trieval and Land Data Assimilation System Development in a Typical Inland River Basin of Chinas Arid Region (ID 5322) aims at improving the monitoring, understand- ing, and predictability of hydrological and ecological pro- cesses at catchment scale, and promote the applicability of quantitative remote sensing in watershed science. Ex- isting Earth Observation platforms provided by the Euro- pean Space Agency as well as prototype airborne systems developed in China - ENVISAT/AATSR, ALOS/PRISM and PALSAR, Airborne LIDAR - are used and combined to retrieve advanced land surface physical properties over high elevation arid regions of China. The existing syn- ergies between this project, the CEOP-AEGIS project (FP7) and the WATER project (CAS) provide incentives for innovative studies. The investigations presented in the following report focus on the development of advanced and innovative methodologies and algorithms to monitor both the state and the trend of key eco-hydrological vari- ables: 3D vegetation properties, land surface evaporation, glacier mass balance and drought indicators.

Fit to predict? Eco-informatics for predicting the catchability of a pelagic fish in near real time.

PubMed

Scales, Kylie L; Hazen, Elliott L; Maxwell, Sara M; Dewar, Heidi; Kohin, Suzanne; Jacox, Michael G; Edwards, Christopher A; Briscoe, Dana K; Crowder, Larry B; Lewison, Rebecca L; Bograd, Steven J

2017-12-01

The ocean is a dynamic environment inhabited by a diverse array of highly migratory species, many of which are under direct exploitation in targeted fisheries. The timescales of variability in the marine realm coupled with the extreme mobility of ocean-wandering species such as tuna and billfish complicates fisheries management. Developing eco-informatics solutions that allow for near real-time prediction of the distributions of highly mobile marine species is an important step towards the maturation of dynamic ocean management and ecological forecasting. Using 25 yr (1990-2014) of NOAA fisheries' observer data from the California drift gillnet fishery, we model relative probability of occurrence (presence-absence) and catchability (total catch per gillnet set) of broadbill swordfish Xiphias gladius in the California Current System. Using freely available environmental data sets and open source software, we explore the physical drivers of regional swordfish distribution. Comparing models built upon remotely sensed data sets with those built upon a data-assimilative configuration of the Regional Ocean Modelling System (ROMS), we explore trade-offs in model construction, and address how physical data can affect predictive performance and operational capacity. Swordfish catchability was found to be highest in deeper waters (>1,500 m) with surface temperatures in the 14-20°C range, isothermal layer depth (ILD) of 20-40 m, positive sea surface height (SSH) anomalies, and during the new moon (<20% lunar illumination). We observed a greater influence of mesoscale variability (SSH, wind speed, isothermal layer depth, eddy kinetic energy) in driving swordfish catchability (total catch) than was evident in predicting the relative probability of presence (presence-absence), confirming the utility of generating spatiotemporally dynamic predictions. Data-assimilative ROMS circumvent the limitations of satellite remote sensing in providing physical data fields for species distribution models (e.g., cloud cover, variable resolution, subsurface data), and facilitate broad-scale prediction of dynamic species distributions in near real time. © 2017 by the Ecological Society of America.

Assimilating leaf area index of three typical types of subtropical forest in China from MODIS time series data based on the integrated ensemble Kalman filter and PROSAIL model

NASA Astrophysics Data System (ADS)

Li, Xuejian; Mao, Fangjie; Du, Huaqiang; Zhou, Guomo; Xu, Xiaojun; Han, Ning; Sun, Shaobo; Gao, Guolong; Chen, Liang

2017-04-01

Subtropical forest ecosystems play essential roles in the global carbon cycle and in carbon sequestration functions, which challenge the traditional understanding of the main functional areas of carbon sequestration in the temperate forests of Europe and America. The leaf area index (LAI) is an important biological parameter in the spatiotemporal simulation of the carbon cycle, and it has considerable significance in carbon cycle research. Dynamic retrieval based on remote sensing data is an important method with which to obtain large-scale high-accuracy assessments of LAI. This study developed an algorithm for assimilating LAI dynamics based on an integrated ensemble Kalman filter using MODIS LAI data, MODIS reflectance data, and canopy reflectance data modeled by PROSAIL, for three typical types of subtropical forest (Moso bamboo forest, Lei bamboo forest, and evergreen and deciduous broadleaf forest) in China during 2014-2015. There were some errors of assimilation in winter, because of the bad data quality of the MODIS product. Overall, the assimilated LAI well matched the observed LAI, with R2 of 0.82, 0.93, and 0.87, RMSE of 0.73, 0.49, and 0.42, and aBIAS of 0.50, 0.23, and 0.03 for Moso bamboo forest, Lei bamboo forest, and evergreen and deciduous broadleaf forest, respectively. The algorithm greatly decreased the uncertainty of the MODIS LAI in the growing season and it improved the accuracy of the MODIS LAI. The advantage of the algorithm is its use of biophysical parameters (e.g., measured LAI) in the LAI assimilation, which makes it possible to assimilate long-term MODIS LAI time series data, and to provide high-accuracy LAI data for the study of carbon cycle characteristics in subtropical forest ecosystems.

Evaluation of a Soil Moisture Data Assimilation System Over West Africa

NASA Astrophysics Data System (ADS)

Bolten, J. D.; Crow, W.; Zhan, X.; Jackson, T.; Reynolds, C.

2009-05-01

A crucial requirement of global crop yield forecasts by the U.S. Department of Agriculture (USDA) International Production Assessment Division (IPAD) is the regional characterization of surface and sub-surface soil moisture. However, due to the spatial heterogeneity and dynamic nature of precipitation events and resulting soil moisture, accurate estimation of regional land surface-atmosphere interactions based sparse ground measurements is difficult. IPAD estimates global soil moisture using daily estimates of minimum and maximum temperature and precipitation applied to a modified Palmer two-layer soil moisture model which calculates the daily amount of soil moisture withdrawn by evapotranspiration and replenished by precipitation. We attempt to improve upon the existing system by applying an Ensemble Kalman filter (EnKF) data assimilation system to integrate surface soil moisture retrievals from the NASA Advanced Microwave Scanning Radiometer (AMSR-E) into the USDA soil moisture model. This work aims at evaluating the utility of merging satellite-retrieved soil moisture estimates with the IPAD two-layer soil moisture model used within the DBMS. We present a quantitative analysis of the assimilated soil moisture product over West Africa (9°N- 20°N; 20°W-20°E). This region contains many key agricultural areas and has a high agro- meteorological gradient from desert and semi-arid vegetation in the North, to grassland, trees and crops in the South, thus providing an ideal location for evaluating the assimilated soil moisture product over multiple land cover types and conditions. A data denial experimental approach is utilized to isolate the added utility of integrating remotely-sensed soil moisture by comparing assimilated soil moisture results obtained using (relatively) low-quality precipitation products obtained from real-time satellite imagery to baseline model runs forced with higher quality rainfall. An analysis of root-zone anomalies for each model simulation suggests that the assimilation of AMSR-E surface soil moisture retrievals can add significant value to USDA root-zone predictions derived from real-time satellite precipitation products.

Exploring Models and Data for Remote Sensing Image Caption Generation

NASA Astrophysics Data System (ADS)

Lu, Xiaoqiang; Wang, Binqiang; Zheng, Xiangtao; Li, Xuelong

2018-04-01

Inspired by recent development of artificial satellite, remote sensing images have attracted extensive attention. Recently, noticeable progress has been made in scene classification and target detection.However, it is still not clear how to describe the remote sensing image content with accurate and concise sentences. In this paper, we investigate to describe the remote sensing images with accurate and flexible sentences. First, some annotated instructions are presented to better describe the remote sensing images considering the special characteristics of remote sensing images. Second, in order to exhaustively exploit the contents of remote sensing images, a large-scale aerial image data set is constructed for remote sensing image caption. Finally, a comprehensive review is presented on the proposed data set to fully advance the task of remote sensing caption. Extensive experiments on the proposed data set demonstrate that the content of the remote sensing image can be completely described by generating language descriptions. The data set is available at https://github.com/201528014227051/RSICD_optimal

Research on active imaging information transmission technology of satellite borne quantum remote sensing

NASA Astrophysics Data System (ADS)

Bi, Siwen; Zhen, Ming; Yang, Song; Lin, Xuling; Wu, Zhiqiang

2017-08-01

According to the development and application needs of Remote Sensing Science and technology, Prof. Siwen Bi proposed quantum remote sensing. Firstly, the paper gives a brief introduction of the background of quantum remote sensing, the research status and related researches at home and abroad on the theory, information mechanism and imaging experiments of quantum remote sensing and the production of principle prototype.Then, the quantization of pure remote sensing radiation field, the state function and squeezing effect of quantum remote sensing radiation field are emphasized. It also describes the squeezing optical operator of quantum light field in active imaging information transmission experiment and imaging experiments, achieving 2-3 times higher resolution than that of coherent light detection imaging and completing the production of quantum remote sensing imaging prototype. The application of quantum remote sensing technology can significantly improve both the signal-to-noise ratio of information transmission imaging and the spatial resolution of quantum remote sensing .On the above basis, Prof.Bi proposed the technical solution of active imaging information transmission technology of satellite borne quantum remote sensing, launched researches on its system composition and operation principle and on quantum noiseless amplifying devices, providing solutions and technical basis for implementing active imaging information technology of satellite borne Quantum Remote Sensing.

Introduction to the physics and techniques of remote sensing

NASA Technical Reports Server (NTRS)

Elachi, Charles

1987-01-01

This book presents a comprehensive overview of the basics behind remote-sensing physics, techniques, and technology. The physics of wave/matter interactions, techniques of remote sensing across the electromagnetic spectrum, and the concepts behind remote sensing techniques now established and future ones under development are discussed. Applications of remote sensing are described for a wide variety of earth and planetary atmosphere and surface sciences. Solid surface sensing across the electromagnetic spectrum, ocean surface sensing, basic principles of atmospheric sensing and radiative transfer, and atmospheric remote sensing in the microwave, millimeter, submillimeter, and infrared regions are examined.

Efficient Data Assimilation Algorithms for Bathymetry Applications

NASA Astrophysics Data System (ADS)

Ghorbanidehno, H.; Kokkinaki, A.; Lee, J. H.; Farthing, M.; Hesser, T.; Kitanidis, P. K.; Darve, E. F.

2016-12-01

Information on the evolving state of the nearshore zone bathymetry is crucial to shoreline management, recreational safety, and naval operations. The high cost and complex logistics of using ship-based surveys for bathymetry estimation have encouraged the use of remote sensing monitoring. Data assimilation methods combine monitoring data and models of nearshore dynamics to estimate the unknown bathymetry and the corresponding uncertainties. Existing applications have been limited to the basic Kalman Filter (KF) and the Ensemble Kalman Filter (EnKF). The former can only be applied to low-dimensional problems due to its computational cost; the latter often suffers from ensemble collapse and uncertainty underestimation. This work explores the use of different variants of the Kalman Filter for bathymetry applications. In particular, we compare the performance of the EnKF to the Unscented Kalman Filter and the Hierarchical Kalman Filter, both of which are KF variants for non-linear problems. The objective is to identify which method can better handle the nonlinearities of nearshore physics, while also having a reasonable computational cost. We present two applications; first, the bathymetry of a synthetic one-dimensional cross section normal to the shore is estimated from wave speed measurements. Second, real remote measurements with unknown error statistics are used and compared to in situ bathymetric survey data collected at the USACE Field Research Facility in Duck, NC. We evaluate the information content of different data sets and explore the impact of measurement error and nonlinearities.

[Thematic Issue: Remote Sensing.

ERIC Educational Resources Information Center

Howkins, John, Ed.

1978-01-01

Four of the articles in this publication discuss the remote sensing of the Earth and its resources by satellites. Among the topics dealt with are the development and management of remote sensing systems, types of satellites used for remote sensing, the uses of remote sensing, and issues involved in using information obtained through remote…

75 FR 65304 - Advisory Committee on Commercial Remote Sensing (ACCRES); Request for Nominations

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-22

... Commercial Remote Sensing (ACCRES); Request for Nominations AGENCY: National Oceanic and Atmospheric... Commercial Remote Sensing (ACCRES). SUMMARY: The Advisory Committee on Commercial Remote Sensing (ACCRES) was... Atmosphere, on matters relating to the U.S. commercial remote sensing industry and NOAA's activities to carry...

Assimilation of thermospheric measurements for ionosphere-thermosphere state estimation

NASA Astrophysics Data System (ADS)

Miladinovich, Daniel S.; Datta-Barua, Seebany; Bust, Gary S.; Makela, Jonathan J.

2016-12-01

We develop a method that uses data assimilation to estimate ionospheric-thermospheric (IT) states during midlatitude nighttime storm conditions. The algorithm Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE) uses time-varying electron densities in the F region, derived primarily from total electron content data, to estimate two drivers of the IT: neutral winds and electric potential. A Kalman filter is used to update background models based on ingested plasma densities and neutral wind measurements. This is the first time a Kalman filtering technique is used with the EMPIRE algorithm and the first time neutral wind measurements from 630.0 nm Fabry-Perot interferometers (FPIs) are ingested to improve estimates of storm time ion drifts and neutral winds. The effects of assimilating remotely sensed neutral winds from FPI observations are studied by comparing results of ingesting: electron densities (N) only, N plus half the measurements from a single FPI, and then N plus all of the FPI data. While estimates of ion drifts and neutral winds based on N give estimates similar to the background models, this study's results show that ingestion of the FPI data can significantly change neutral wind and ion drift estimation away from background models. In particular, once neutral winds are ingested, estimated neutral winds agree more with validation wind data, and estimated ion drifts in the magnetic field-parallel direction are more sensitive to ingestion than the field-perpendicular zonal and meridional directions. Also, data assimilation with FPI measurements helps provide insight into the effects of contamination on 630.0 nm emissions experienced during geomagnetic storms.

Exploring and Analyzing Climate Variations Online by Using NASA MERRA-2 Data at GES DISC

NASA Technical Reports Server (NTRS)

Shen, Suhung; Ostrenga, Dana M.; Vollmer, Bruce E.; Kempler, Steven J.

2016-01-01

NASA Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) (http:giovanni.sci.gsfc.nasa.govgiovanni) is a web-based data visualization and analysis system developed by the Goddard Earth Sciences Data and Information Services Center (GES DISC). Current data analysis functions include Lat-Lon map, time series, scatter plot, correlation map, difference, cross-section, vertical profile, and animation etc. The system enables basic statistical analysis and comparisons of multiple variables. This web-based tool facilitates data discovery, exploration and analysis of large amount of global and regional remote sensing and model data sets from a number of NASA data centers. Long term global assimilated atmospheric, land, and ocean data have been integrated into the system that enables quick exploration and analysis of climate data without downloading, preprocessing, and learning data. Example data include climate reanalysis data from NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) which provides data beginning in 1980 to present; land data from NASA Global Land Data Assimilation System (GLDAS), which assimilates data from 1948 to 2012; as well as ocean biological data from NASA Ocean Biogeochemical Model (NOBM), which provides data from 1998 to 2012. This presentation, using surface air temperature, precipitation, ozone, and aerosol, etc. from MERRA-2, demonstrates climate variation analysis with Giovanni at selected regions.

Literature relevant to remote sensing of water quality

NASA Technical Reports Server (NTRS)

Middleton, E. M.; Marcell, R. F.

1983-01-01

References relevant to remote sensing of water quality were compiled, organized, and cross-referenced. The following general categories were included: (1) optical properties and measurement of water characteristics; (2) interpretation of water characteristics by remote sensing, including color, transparency, suspended or dissolved inorganic matter, biological materials, and temperature; (3) application of remote sensing for water quality monitoring; (4) application of remote sensing according to water body type; and (5) manipulation, processing and interpretation of remote sensing digital water data.

Learning Methods of Remote Sensing In the 2013 Curriculum of Secondary School

NASA Astrophysics Data System (ADS)

Lili Somantri, Nandi

2016-11-01

The new remote sensing material included in the subjects of geography in the curriculum of 1994. For geography teachers generation of 90s and over who in college do not get the material remote sensing, for teaching is a tough matter. Most teachers only give a theoretical matter, and do not carry out practical reasons in the lack of facilities and infrastructure of computer laboratories. Therefore, in this paper studies the importance about the method or manner of teaching remote sensing material in schools. The purpose of this paper is 1) to explain the position of remote sensing material in the study of geography, 2) analyze the Geography Curriculum 2013 Subjects related to remote sensing material, 3) describes a method of teaching remote sensing material in schools. The method used in this paper is a descriptive analytical study supported by the literature. The conclusion of this paper that the position of remote sensing in the study of geography is a method or a way to obtain spatial data earth's surface. In the 2013 curriculum remote sensing material has been applied to the study of land use and transportation. Remote sensing methods of teaching must go through a practicum, which starts from the introduction of the theory of remote sensing, data extraction phase of remote sensing imagery to produce maps, both visually and digitally, field surveys, interpretation of test accuracy, and improved maps.

NASA Tech Briefs, April 2012

NASA Technical Reports Server (NTRS)

2012-01-01

Topics include: Computational Ghost Imaging for Remote Sensing; Digital Architecture for a Trace Gas Sensor Platform; Dispersed Fringe Sensing Analysis - DFSA; Indium Tin Oxide Resistor-Based Nitric Oxide Microsensors; Gas Composition Sensing Using Carbon Nanotube Arrays; Sensor for Boundary Shear Stress in Fluid Flow; Model-Based Method for Sensor Validation; Qualification of Engineering Camera for Long-Duration Deep Space Missions; Remotely Powered Reconfigurable Receiver for Extreme Environment Sensing Platforms; Bump Bonding Using Metal-Coated Carbon Nanotubes; In Situ Mosaic Brightness Correction; Simplex GPS and InSAR Inversion Software; Virtual Machine Language 2.1; Multi-Scale Three-Dimensional Variational Data Assimilation System for Coastal Ocean Prediction; Pandora Operation and Analysis Software; Fabrication of a Cryogenic Bias Filter for Ultrasensitive Focal Plane; Processing of Nanosensors Using a Sacrificial Template Approach; High-Temperature Shape Memory Polymers; Modular Flooring System; Non-Toxic, Low-Freezing, Drop-In Replacement Heat Transfer Fluids; Materials That Enhance Efficiency and Radiation Resistance of Solar Cells; Low-Cost, Rugged High-Vacuum System; Static Gas-Charging Plug; Floating Oil-Spill Containment Device; Stemless Ball Valve; Improving Balance Function Using Low Levels of Electrical Stimulation of the Balance Organs; Oxygen-Methane Thruster; Lunar Navigation Determination System - LaNDS; Launch Method for Kites in Low-Wind or No-Wind Conditions; Supercritical CO2 Cleaning System for Planetary Protection and Contamination Control Applications; Design and Performance of a Wideband Radio Telescope; Finite Element Models for Electron Beam Freeform Fabrication Process Autonomous Information Unit for Fine-Grain Data Access Control and Information Protection in a Net-Centric System; Vehicle Detection for RCTA/ANS (Autonomous Navigation System); Image Mapping and Visual Attention on the Sensory Ego-Sphere; HyDE Framework for Stochastic and Hybrid Model-Based Diagnosis; and IMAGESEER - IMAGEs for Education and Research.

JPRS Report, Science & Technology, China, Remote Sensing Systems, Applications.

DTIC Science & Technology

1991-01-17

Partial Contents: Short Introduction to Nation’s Remote Sensing Units, Domestic Airborne Remote - Sensing System, Applications in Monitoring Natural...Disasters, Applications of Imagery From Experimental Satellites Launched in 1985, 1986, Current Status, Future Prospects for Domestic Remote - Sensing -Satellite...Ground Station, and Radar Remote - Sensing Technology Used to Monitor Yellow River Delta,

Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites

NASA Astrophysics Data System (ADS)

Aalstad, Kristoffer; Westermann, Sebastian; Vikhamar Schuler, Thomas; Boike, Julia; Bertino, Laurent

2018-01-01

With its high albedo, low thermal conductivity and large water storing capacity, snow strongly modulates the surface energy and water balance, which makes it a critical factor in mid- to high-latitude and mountain environments. However, estimating the snow water equivalent (SWE) is challenging in remote-sensing applications already at medium spatial resolutions of 1 km. We present an ensemble-based data assimilation framework that estimates the peak subgrid SWE distribution (SSD) at the 1 km scale by assimilating fractional snow-covered area (fSCA) satellite retrievals in a simple snow model forced by downscaled reanalysis data. The basic idea is to relate the timing of the snow cover depletion (accessible from satellite products) to the peak SSD. Peak subgrid SWE is assumed to be lognormally distributed, which can be translated to a modeled time series of fSCA through the snow model. Assimilation of satellite-derived fSCA facilitates the estimation of the peak SSD, while taking into account uncertainties in both the model and the assimilated data sets. As an extension to previous studies, our method makes use of the novel (to snow data assimilation) ensemble smoother with multiple data assimilation (ES-MDA) scheme combined with analytical Gaussian anamorphosis to assimilate time series of Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 fSCA retrievals. The scheme is applied to Arctic sites near Ny-Ålesund (79° N, Svalbard, Norway) where field measurements of fSCA and SWE distributions are available. The method is able to successfully recover accurate estimates of peak SSD on most of the occasions considered. Through the ES-MDA assimilation, the root-mean-square error (RMSE) for the fSCA, peak mean SWE and peak subgrid coefficient of variation is improved by around 75, 60 and 20 %, respectively, when compared to the prior, yielding RMSEs of 0.01, 0.09 m water equivalent (w.e.) and 0.13, respectively. The ES-MDA either outperforms or at least nearly matches the performance of other ensemble-based batch smoother schemes with regards to various evaluation metrics. Given the modularity of the method, it could prove valuable for a range of satellite-era hydrometeorological reanalyses.

[A review on polarization information in the remote sensing detection].

PubMed

Gong, Jie-Qiong; Zhan, Hai-Gang; Liu, Da-Zhao

2010-04-01

Polarization is one of the inherent characteristics. Because the surface of the target structure, internal structure, and the angle of incident light are different, the earth's surface and any target in atmosphere under optical interaction process will have their own characteristic nature of polarization. Polarimetric characteristics of radiation energy from the targets are used in polarization remote sensing detection as detective information. Polarization remote sensing detection can get the seven-dimensional information of targets in complicated backgrounds, detect well-resolved outline of targets and low-reflectance region of objectives, and resolve the problems of atmospheric detection and identification camouflage detection which the traditional remote sensing detection can not solve, having good foreground in applications. This paper introduces the development of polarization information in the remote sensing detection from the following four aspects. The rationale of polarization remote sensing detection is the base of polarization remote sensing detection, so it is firstly introduced. Secondly, the present researches on equipments that are used in polarization remote sensing detection are particularly and completely expatiated. Thirdly, the present exploration of theoretical simulation of polarization remote sensing detection is well detailed. Finally, the authors present the applications research home and abroad of the polarization remote sensing detection technique in the fields of remote sensing, atmospheric sounding, sea surface and underwater detection, biology and medical diagnosis, astronomical observation and military, summing up the current problems in polarization remote sensing detection. The development trend of polarization remote sensing detection technology in the future is pointed out in order to provide a reference for similar studies.

Soil Moisture and Temperature Measuring Networks in the Tibetan Plateau and Their Hydrological Applications

NASA Astrophysics Data System (ADS)

Yang, Kun; Chen, Yingying; Qin, Jun; Lu, Hui

2017-04-01

Multi-sphere interactions over the Tibetan Plateau directly impact its surrounding climate and environment at a variety of spatiotemporal scales. Remote sensing and modeling are expected to provide hydro-meteorological data needed for these process studies, but in situ observations are required to support their calibration and validation. For this purpose, we have established two networks on the Tibetan Plateau to measure densely two state variables (soil moisture and temperature) and four soil depths (0 5, 10, 20, and 40 cm). The experimental area is characterized by low biomass, high soil moisture dynamic range, and typical freeze-thaw cycle. As auxiliary parameters of these networks, soil texture and soil organic carbon content are measured at each station to support further studies. In order to guarantee continuous and high-quality data, tremendous efforts have been made to protect the data logger from soil water intrusion, to calibrate soil moisture sensors, and to upscale the point measurements. One soil moisture network is located in a semi-humid area in central Tibetan Plateau (Naqu), which consists of 56 stations with their elevation varying over 4470 4950 m and covers three spatial scales (1.0, 0.3, 0.1 degree). The other is located in a semi-arid area in southern Tibetan Plateau (Pali), which consists of 25 stations and covers an area of 0.25 degree. The spatiotemporal characteristics of the former network were analyzed, and a new spatial upscaling method was developed to obtain the regional mean soil moisture truth from the point measurements. Our networks meet the requirement for evaluating a variety of soil moisture products, developing new algorithms, and analyzing soil moisture scaling. Three applications with the network data are presented in this paper. 1. Evaluation of Current remote sensing and LSM products. The in situ data have been used to evaluate AMSR-E, AMSR2, SMOS and SMAP products and four modeled outputs by the Global Land Data Assimilation System (GLDAS). 2. Development of New Products. We developed a dual-pass land data assimilation system. The essential idea of the system is to calibrate a land data assimilation system before a normal data assimilation. The calibration is based on satellite data rather than in situ data. Through this way, we may alleviate the impact of uncertainties in determining the error covariance of both observation operator and model operation, as it is always tough to determine the covariance. The performance of the data assimilation system is presented through comparison against the Tibetan Plateau soil moisture measuring networks. And the results are encouraging. 3. Estimation of Soil Parameter Values in a Land Surface Model. We explored the possibility to estimate soil parameter values by assimilating AMSR-E brightness temperature (TB) data. In the assimilation system, the TB is simulated by the coupled system of a land surface model (LSM) and a radiative transfer model (RTM), and the simulation errors highly depend on parameters in both the LSM and the RTM. Thus, sensitive soil parameters may be inversely estimated through minimizing the TB errors. The effectiveness of the estimated parameter values is evaluated against intensive measurements of soil parameters and soil moisture in three grasslands of the Tibetan Plateau and the Mongolian Plateau. The results indicate that this satellite data-based approach can improve the data quality of soil porosity, a key parameter for soil moisture modeling, and LSM simulations with the estimated parameter values reasonably reproduce the measured soil moisture. This demonstrates it is feasible to calibrate LSMs for soil moisture simulations at grid scale by assimilating microwave satellite data, although more efforts are expected to improve the robustness of the model calibration.

Physics Guided Data Science in the Earth Sciences

NASA Astrophysics Data System (ADS)

Ganguly, A. R.

2017-12-01

Even as the geosciences are becoming relatively data-rich owing to remote sensing and archived model simulations, established physical understanding and process knowledge cannot be ignored. The ability to leverage both physics and data-intensive sciences may lead to new discoveries and predictive insights. A principled approach to physics guided data science, where physics informs feature selection, output constraints, and even the architecture of the learning models, is motivated. The possibility of hybrid physics and data science models at the level of component processes is discussed. The challenges and opportunities, as well as the relations to other approaches such as data assimilation - which also bring physics and data together - are discussed. Case studies are presented in climate, hydrology and meteorology.

Cybernetic Basis and System Practice of Remote Sensing and Spatial Information Science

NASA Astrophysics Data System (ADS)

Tan, X.; Jing, X.; Chen, R.; Ming, Z.; He, L.; Sun, Y.; Sun, X.; Yan, L.

2017-09-01

Cybernetics provides a new set of ideas and methods for the study of modern science, and it has been fully applied in many areas. However, few people have introduced cybernetics into the field of remote sensing. The paper is based on the imaging process of remote sensing system, introducing cybernetics into the field of remote sensing, establishing a space-time closed-loop control theory for the actual operation of remote sensing. The paper made the process of spatial information coherently, and improved the comprehensive efficiency of the space information from acquisition, procession, transformation to application. We not only describes the application of cybernetics in remote sensing platform control, sensor control, data processing control, but also in whole system of remote sensing imaging process control. We achieve the information of output back to the input to control the efficient operation of the entire system. This breakthrough combination of cybernetics science and remote sensing science will improve remote sensing science to a higher level.

A preliminary experiment for the long-term regional reanalysis over Japan assimilating conventional observations with NHM-LETKF

NASA Astrophysics Data System (ADS)

Fukui, Shin; Iwasaki, Toshiki; Saito, Kazuo; Seko, Hiromu; Kunii, Masaru

2016-04-01

Several long-term global reanalyses have been produced by major operational centres and have contributed to the advance of weather and climate researches considerably. Although the horizontal resolutions of these global reanalyses are getting higher partly due to the development of computing technology, they are still too coarse to reproduce local circulations and precipitation realistically. To solve this problem, dynamical downscaling is often employed. However, the forcing from lateral boundaries only cannot necessarily control the inner fields especially in long-term dynamical downscaling. Regional reanalysis is expected to overcome the difficulty. To maintain the long-term consistency of the analysis quality, it is better to assimilate only the conventional observations that are available in long period. To confirm the effectiveness of the regional reanalysis, some assimilation experiments are performed. In the experiments, only conventional observations (SYNOP, SHIP, BUOY, TEMP, PILOT, TC-Bogus) are assimilated with the NHM-LETKF system, which consists of the nonhydrostatic model (NHM) of the Japan Meteorological Agency (JMA) and the local ensemble transform Kalman filter (LETKF). The horizontal resolution is 25 km and the domain covers Japan and its surroundings. Japanese 55-year reanalysis (JRA-55) is adopted as the initial and lateral boundary conditions for the NHM-LETKF forecast-analysis cycles. The ensemble size is 10. The experimental period is August 2014 as a representative of warm season for the region. The results are verified against the JMA's operational Meso-scale Analysis, which is produced with assimilating observation data including various remote sensing observations using a 4D-Var scheme, and compared with those of the simple dynamical downscaling experiment without data assimilation. Effects of implementation of lateral boundary perturbations derived from an EOF analysis of JRA-55 over the targeted domain are also examined. The comparison proposes that the assimilation system can reproduce more accurate fields than dynamical downscaling. The implementation of the lateral boundary perturbations implies that the perturbations contribute to providing more appropriate ensemble spreads, though the perturbations are not necessarily consistent to those of the inner fields given by NHM-LETKF.

A 15-year global biogeochemical reanalysis with ocean colour data assimilation

NASA Astrophysics Data System (ADS)

Ford, David; Barciela, Rosa

2013-04-01

A continuous global time-series of remotely sensed ocean colour observations is available from 1997 to the present day. However coverage is incomplete, and limited to the sea surface. Models are therefore required to provide full spatial coverage, and to investigate the relationships between physical and biological variables and the carbon cycle. Data assimilation can then be used to constrain models to fit the observations, thereby combining the advantages of both sources of information. As part of the European Space Agency's Climate Change Initiative (ESA-CCI), we assimilate chlorophyll concentration derived from ocean colour observations into a coupled physical-biogeochemical model. The data assimilation scheme (Hemmings et al., 2008, J. Mar. Res.; Ford et al., 2012, Ocean Sci.) uses the information from the observations to update all biological and carbon cycle state variables within the model. Global daily reanalyses have been produced, with and without assimilation of merged ocean colour data provided by GlobColour, for the period September 1997 to August 2012. The assimilation has been shown to significantly improve the model's representation of chlorophyll concentration, at the surface and at depth. Furthermore, there is evidence of improvement to the representation of pCO2, nutrients and zooplankton concentration compared to in situ observations. We use the results to quantify recent seasonal and inter-annual variability in variables including chlorophyll concentration, air-sea CO2 flux and alkalinity. In particular, we explore the impact of physical drivers such as the El Niño Southern Oscillation (ENSO) on the model's representation of chlorophyll and the carbon cycle, and the pros and cons of the model reanalyses compared to observation-based climatologies. Furthermore, we perform a comparison between the GlobColour product and an initial version of a new merged product being developed as part of the ESA-CCI. Equivalent year-long hindcasts are performed with assimilation of each data set, and compared to a control run. Differences in the products are discussed, along with their impact on model accuracy compared to in situ observations, and the representation of the carbon cycle in each hindcast.

Soil moisture and properties estimation by assimilating soil temperatures using particle batch smoother: A new perspective for DTS

NASA Astrophysics Data System (ADS)

Dong, J.; Steele-Dunne, S. C.; Ochsner, T. E.; Van De Giesen, N.

2015-12-01

Soil moisture, hydraulic and thermal properties are critical for understanding the soil surface energy balance and hydrological processes. Here, we will discuss the potential of using soil temperature observations from Distributed Temperature Sensing (DTS) to investigate the spatial variability of soil moisture and soil properties. With DTS soil temperature can be measured with high resolution (spatial <1m, and temporal < 1min) in cables up to kilometers in length. Soil temperature evolution is primarily controlled by the soil thermal properties, and the energy balance at the soil surface. Hence, soil moisture, which affects both soil thermal properties and the energy that participates the evaporation process, is strongly correlated to the soil temperatures. In addition, the dynamics of the soil moisture is determined by the soil hydraulic properties.Here we will demonstrate that soil moisture, hydraulic and thermal properties can be estimated by assimilating observed soil temperature at shallow depths using the Particle Batch Smoother (PBS). The PBS can be considered as an extension of the particle filter, which allows us to infer soil moisture and soil properties using the dynamics of soil temperature within a batch window. Both synthetic and real field data will be used to demonstrate the robustness of this approach. We will show that the proposed method is shown to be able to handle different sources of uncertainties, which may provide a new view of using DTS observations to estimate sub-meter resolution soil moisture and properties for remote sensing product validation.

Near-earth orbital guidance and remote sensing

NASA Technical Reports Server (NTRS)

Powers, W. F.

1972-01-01

The curriculum of a short course in remote sensing and parameter optimization is presented. The subjects discussed are: (1) basics of remote sensing and the user community, (2) multivariant spectral analysis, (3) advanced mathematics and physics of remote sensing, (4) the atmospheric environment, (5) imaging sensing, and (6)nonimaging sensing. Mathematical models of optimization techniques are developed.

Operational programs in forest management and priority in the utilization of remote sensing

NASA Technical Reports Server (NTRS)

Douglass, R. W.

1978-01-01

A speech is given on operational remote sensing programs in forest management and the importance of remote sensing in forestry is emphasized. Forest service priorities in using remote sensing are outlined.

Remote sensing, land use, and demography - A look at people through their effects on the land

NASA Technical Reports Server (NTRS)

Paul, C. K.; Landini, A. J.

1976-01-01

Relevant causes of failure by the remote sensing community in the urban scene are analyzed. The reasons for the insignificant role of remote sensing in urban land use data collection are called the law of realism, the incompatibility of remote sensing and urban management system data formats is termed the law of nominal/ordinal systems compatibility, and the land use/population correlation dilemma is referred to as the law of missing persons. The study summarizes the three laws of urban land use information for which violations, avoidance, or ignorance have caused the decline of present remote sensing research. Particular attention is given to the rationale for urban land use information and for remote sensing. It is shown that remote sensing of urban land uses compatible with the three laws can be effectively developed by realizing the 10 percent contribution of remote sensing to urban land use planning data collection.

Thematic Conference on Geologic Remote Sensing, 8th, Denver, CO, Apr. 29-May 2, 1991, Proceedings. Vols. 1 & 2

NASA Technical Reports Server (NTRS)

1991-01-01

The proceedings contain papers discussing the state-of-the-art exploration, engineering, and environmental applications of geologic remote sensing, along with the research and development activities aimed at increasing the future capabilities of this technology. The following topics are addressed: spectral geology, U.S. and international hydrocarbon exporation, radar and thermal infrared remote sensing, engineering geology and hydrogeology, mineral exploration, remote sensing for marine and environmental applications, image processing and analysis, geobotanical remote sensing, and data integration and geographic information systems. Particular attention is given to spectral alteration mapping with imaging spectrometers, mapping the coastal plain of the Congo with airborne digital radar, applications of remote sensing techniques to the assessment of dam safety, remote sensing of ferric iron minerals as guides for gold exploration, principal component analysis for alteration mappping, and the application of remote sensing techniques for gold prospecting in the north Fujian province.

Methods of training the graduate level and professional geologist in remote sensing technology

NASA Technical Reports Server (NTRS)

Kolm, K. E.

1981-01-01

Requirements for a basic course in remote sensing to accommodate the needs of the graduate level and professional geologist are described. The course should stress the general topics of basic remote sensing theory, the theory and data types relating to different remote sensing systems, an introduction to the basic concepts of computer image processing and analysis, the characteristics of different data types, the development of methods for geological interpretations, the integration of all scales and data types of remote sensing in a given study, the integration of other data bases (geophysical and geochemical) into a remote sensing study, and geological remote sensing applications. The laboratories should stress hands on experience to reinforce the concepts and procedures presented in the lecture. The geologist should then be encouraged to pursue a second course in computer image processing and analysis of remotely sensed data.

Remote sensing of Earth terrain

NASA Technical Reports Server (NTRS)

Kong, J. A.

1993-01-01

Progress report on remote sensing of Earth terrain covering the period from Jan. to June 1993 is presented. Areas of research include: radiative transfer model for active and passive remote sensing of vegetation canopy; polarimetric thermal emission from rough ocean surfaces; polarimetric passive remote sensing of ocean wind vectors; polarimetric thermal emission from periodic water surfaces; layer model with tandom spheriodal scatterers for remote sensing of vegetation canopy; application of theoretical models to active and passive remote sensing of saline ice; radiative transfer theory for polarimetric remote sensing of pine forest; scattering of electromagnetic waves from a dense medium consisting of correlated mie scatterers with size distributions and applications to dry snow; variance of phase fluctuations of waves propagating through a random medium; polarimetric signatures of a canopy of dielectric cylinders based on first and second order vector radiative transfer theory; branching model for vegetation; polarimetric passive remote sensing of periodic surfaces; composite volume and surface scattering model; and radar image classification.

Investigating impacts of natural and human-induced environmental changes on hydrological processes and flood hazards using a GIS-based hydrological/hydraulic model and remote sensing data

NASA Astrophysics Data System (ADS)

Wang, Lei

Natural and human-induced environmental changes have been altering the earth's surface and hydrological processes, and thus directly contribute to the severity of flood hazards. To understand these changes and their impacts, this research developed a GIS-based hydrological and hydraulic modeling system, which incorporates state-of-the-art remote sensing data to simulate flood under various scenarios. The conceptual framework and technical issues of incorporating multi-scale remote sensing data have been addressed. This research develops an object-oriented hydrological modeling framework. Compared with traditional lumped or cell-based distributed hydrological modeling frameworks, the object-oriented framework allows basic spatial hydrologic units to have various size and irregular shape. This framework is capable of assimilating various GIS and remotely-sensed data with different spatial resolutions. It ensures the computational efficiency, while preserving sufficient spatial details of input data and model outputs. Sensitivity analysis and comparison of high resolution LIDAR DEM with traditional USGS 30m resolution DEM suggests that the use of LIDAR DEMs can greatly reduce uncertainty in calibration of flow parameters in the hydrologic model and hence increase the reliability of modeling results. In addition, subtle topographic features and hydrologic objects like surface depressions and detention basins can be extracted from the high resolution LiDAR DEMs. An innovative algorithm has been developed to efficiently delineate surface depressions and detention basins from LiDAR DEMs. Using a time series of Landsat images, a retrospective analysis of surface imperviousness has been conducted to assess the hydrologic impact of urbanization. The analysis reveals that with rapid urbanization the impervious surface has been increased from 10.1% to 38.4% for the case study area during 1974--2002. As a result, the peak flow for a 100-year flood event has increased by 20% and the floodplain extent has expanded by about 21.6%. The quantitative analysis suggests that the large regional detentions basins have effectively offset the adverse effect of increased impervious surface during the urbanization process. Based on the simulation and scenario analyses of land subsidence and potential climate changes, some planning measures and policy implications have been derived for guiding smart urban growth and sustainable resource development and management to minimize flood hazards.

Remote sensing by satellite - Technical and operational implications for international cooperation

NASA Technical Reports Server (NTRS)

Doyle, S. E.

1976-01-01

International cooperation in the U.S. Space Program is discussed and related to the NASA program for remote sensing of the earth. Satellite remote sensing techniques are considered along with the selection of the best sensors and wavelength bands. The technology of remote sensing satellites is considered with emphasis on the Landsat system configuration. Future aspects of remote sensing satellites are considered.

Remote sensing in operational range management programs in Western Canada

NASA Technical Reports Server (NTRS)

Thompson, M. D.

1977-01-01

A pilot program carried out in Western Canada to test remote sensing under semi-operational conditions and display its applicability to operational range management programs was described. Four agencies were involved in the program, two in Alberta and two in Manitoba. Each had different objectives and needs for remote sensing within its range management programs, and each was generally unfamiliar with remote sensing techniques and their applications. Personnel with experience and expertise in the remote sensing and range management fields worked with the agency personnel through every phase of the pilot program. Results indicate that these agencies have found remote sensing to be a cost effective tool and will begin to utilize remote sensing in their operational work during ensuing seasons.

PROCEEDINGS OF THE FOURTH SYMPOSIUM ON REMOTE SENSING OF ENVIRONMENT; 12, 13, 14 APRIL 1966.

DTIC Science & Technology

The symposium was conducted as part of a continuing program investigating the field of remote sensing , its potential in scientific research and...information on all aspects of remote sensing , with special emphasis on such topics as needs for remotely sensed data, data management, and the special... remote sensing programs, data acquisition, data analysis and application, and equipment design, were presented. (Author)

Remote sensing and image interpretation

NASA Technical Reports Server (NTRS)

Lillesand, T. M.; Kiefer, R. W. (Principal Investigator)

1979-01-01

A textbook prepared primarily for use in introductory courses in remote sensing is presented. Topics covered include concepts and foundations of remote sensing; elements of photographic systems; introduction to airphoto interpretation; airphoto interpretation for terrain evaluation; photogrammetry; radiometric characteristics of aerial photographs; aerial thermography; multispectral scanning and spectral pattern recognition; microwave sensing; and remote sensing from space.

Downscaling SMAP Radiometer Soil Moisture over the CONUS using Soil-Climate Information and Ensemble Learning

NASA Astrophysics Data System (ADS)

Abbaszadeh, P.; Moradkhani, H.

2017-12-01

Soil moisture contributes significantly towards the improvement of weather and climate forecast and understanding terrestrial ecosystem processes. It is known as a key hydrologic variable in the agricultural drought monitoring, flood modeling and irrigation management. While satellite retrievals can provide an unprecedented information on soil moisture at global-scale, the products are generally at coarse spatial resolutions (25-50 km2). This often hampers their use in regional or local studies, which normally require a finer resolution of the data set. This work presents a new framework based on an ensemble learning method while using soil-climate information derived from remote-sensing and ground-based observations to downscale the level 3 daily composite version (L3_SM_P) of SMAP radiometer soil moisture over the Continental U.S. (CONUS) at 1 km spatial resolution. In the proposed method, a suite of remotely sensed and in situ data sets in addition to soil texture information and topography data among others were used. The downscaled product was validated against in situ soil moisture measurements collected from a limited number of core validation sites and several hundred sparse soil moisture networks throughout the CONUS. The obtained results indicated a great potential of the proposed methodology to derive the fine resolution soil moisture information applicable for fine resolution hydrologic modeling, data assimilation and other regional studies.

Estimating patterns in Spartina alterniflora belowground biomass within salt marshes

NASA Astrophysics Data System (ADS)

O'Connell, J. L.; Mishra, D. R.; Alber, M.; Byrd, K. B.

2017-12-01

Belowground biomass of marsh plants, such as Spartina alterniflora, help prevent marsh loss because they promote soil accretion, stabilize soils and add organic matter. However, site-wide estimates of belowground biomass are difficult to obtain because root:shoot ratios vary considerably both within species and across sites. We are working to develop a data fusion tool that can predict key characteristics of S. alterniflora, including belowground biomass and plant canopy N, based on satellite imagery. We used field observations from four salt marsh locations along the Georgia Coast, including one that is studied as part of the Georgia Coastal Ecosystems LTER project. From field and remote-sensing data, we developed a hybrid modeling approach to estimate % foliar N (a surrogate for plant assimilated nutrients). Partial Least squares (PLS) regression analysis of Landsat-8 spectral bands could predict variation in foliar N and belowground biomass, suggesting this public data source might be utilized for site-wide assessment of plant biophysical variables in salt marshes. Spectrally estimated foliar N and aboveground biomass were associated with belowground biomass and root:shoot ratio in S. alterniflora. This mirrors results from a previous study from the Sacramento-San Joaquin Delta, CA, on Scheonoplectus acutus, a marsh plant found in some tidal freshwater marshes. Therefore remote sensing may be a useful tool for measuring whole plant productivity among multiple coastal marsh species.

Comparison of Land Skin Temperature from a Land Model, Remote Sensing, and In-situ Measurement

NASA Technical Reports Server (NTRS)

Wang, Aihui; Barlage, Michael; Zeng, Xubin; Draper, Clara Sophie

2014-01-01

Land skin temperature (Ts) is an important parameter in the energy exchange between the land surface and atmosphere. Here hourly Ts from the Community Land Model Version 4.0, MODIS satellite observations, and in-situ observations in 2003 were compared. Compared with the in-situ observations over four semi-arid stations, both MODIS and modeled Ts show negative biases, but MODIS shows an overall better performance. Global distribution of differences between MODIS and modeled Ts shows diurnal, seasonal, and spatial variations. Over sparsely vegetated areas, the model Ts is generally lower than the MODIS observed Ts during the daytime, while the situation is opposite at nighttime. The revision of roughness length for heat and the constraint of minimum friction velocity from Zeng et al. [2012] bring the modeled Ts closer to MODIS during the day, and have little effect on Ts at night. Five factors contributing to the Ts differences between the model and MODIS are identified, including the difficulty in properly accounting for cloud cover information at the appropriate temporal and spatial resolutions, and uncertainties in surface energy balance computation, atmospheric forcing data, surface emissivity, and MODIS Ts data. These findings have implications for the cross-evaluation of modeled and remotely sensed Ts, as well as the data assimilation of Ts observations into Earth system models.

NASA Earth Science Research Results for Improved Regional Crop Yield Prediction

NASA Astrophysics Data System (ADS)

Mali, P.; O'Hara, C. G.; Shrestha, B.; Sinclair, T. R.; G de Goncalves, L. G.; Salado Navarro, L. R.

2007-12-01

National agencies such as USDA Foreign Agricultural Service (FAS), Production Estimation and Crop Assessment Division (PECAD) work specifically to analyze and generate timely crop yield estimates that help define national as well as global food policies. The USDA/FAS/PECAD utilizes a Decision Support System (DSS) called CADRE (Crop Condition and Data Retrieval Evaluation) mainly through an automated database management system that integrates various meteorological datasets, crop and soil models, and remote sensing data; providing significant contribution to the national and international crop production estimates. The "Sinclair" soybean growth model has been used inside CADRE DSS as one of the crop models. This project uses Sinclair model (a semi-mechanistic crop growth model) for its potential to be effectively used in a geo-processing environment with remote-sensing-based inputs. The main objective of this proposed work is to verify, validate and benchmark current and future NASA earth science research results for the benefit in the operational decision making process of the PECAD/CADRE DSS. For this purpose, the NASA South American Land Data Assimilation System (SALDAS) meteorological dataset is tested for its applicability as a surrogate meteorological input in the Sinclair model meteorological input requirements. Similarly, NASA sensor MODIS products is tested for its applicability in the improvement of the crop yield prediction through improving precision of planting date estimation, plant vigor and growth monitoring. The project also analyzes simulated Visible/Infrared Imager/Radiometer Suite (VIIRS, a future NASA sensor) vegetation product for its applicability in crop growth prediction to accelerate the process of transition of VIIRS research results for the operational use of USDA/FAS/PECAD DSS. The research results will help in providing improved decision making capacity to the USDA/FAS/PECAD DSS through improved vegetation growth monitoring from high spatial and temporal resolution remote sensing datasets; improved time-series meteorological inputs required for crop growth models; and regional prediction capability through geo-processing-based yield modeling.

Bayesian estimation and use of high-throughput remote sensing indices for quantitative genetic analyses of leaf growth.

PubMed

Baker, Robert L; Leong, Wen Fung; An, Nan; Brock, Marcus T; Rubin, Matthew J; Welch, Stephen; Weinig, Cynthia

2018-02-01

We develop Bayesian function-valued trait models that mathematically isolate genetic mechanisms underlying leaf growth trajectories by factoring out genotype-specific differences in photosynthesis. Remote sensing data can be used instead of leaf-level physiological measurements. Characterizing the genetic basis of traits that vary during ontogeny and affect plant performance is a major goal in evolutionary biology and agronomy. Describing genetic programs that specifically regulate morphological traits can be complicated by genotypic differences in physiological traits. We describe the growth trajectories of leaves using novel Bayesian function-valued trait (FVT) modeling approaches in Brassica rapa recombinant inbred lines raised in heterogeneous field settings. While frequentist approaches estimate parameter values by treating each experimental replicate discretely, Bayesian models can utilize information in the global dataset, potentially leading to more robust trait estimation. We illustrate this principle by estimating growth asymptotes in the face of missing data and comparing heritabilities of growth trajectory parameters estimated by Bayesian and frequentist approaches. Using pseudo-Bayes factors, we compare the performance of an initial Bayesian logistic growth model and a model that incorporates carbon assimilation (A max ) as a cofactor, thus statistically accounting for genotypic differences in carbon resources. We further evaluate two remotely sensed spectroradiometric indices, photochemical reflectance (pri2) and MERIS Terrestrial Chlorophyll Index (mtci) as covariates in lieu of A max , because these two indices were genetically correlated with A max across years and treatments yet allow much higher throughput compared to direct leaf-level gas-exchange measurements. For leaf lengths in uncrowded settings, including A max improves model fit over the initial model. The mtci and pri2 indices also outperform direct A max measurements. Of particular importance for evolutionary biologists and plant breeders, hierarchical Bayesian models estimating FVT parameters improve heritabilities compared to frequentist approaches.

Temporal Variability of Canopy Light Use Efficiency and its Environmental Controls in a Subtropical Mangrove Wetland

NASA Astrophysics Data System (ADS)

Zhu, X.

2016-12-01

Mangrove wetlands play an important role in global carbon cycle due to their strong carbon sequestration resulting from high plant carbon assimilation and low soil respiration. However, temporal variability of carbon sequestration in mangrove wetlands is less understood since carbon processes of mangrove wetlands are influenced by many complicated and concurrent environmental controls including tidal activities, site climate and soil conditions. Canopy light use efficiency (LUE), is the most important plant physiological parameter that can be used to describe the temporal dynamics of canopy photosynthesis, and therefore a better characterization of temporal variability of canopy LUE will improve our understanding in mangrove photosynthesis and carbon balance. One of our aims is to study the temporal variability of canopy LUE and its environmental controls in a subtropical mangrove wetland. Half-hourly canopy LUE is derived from eddy covariance (EC) carbon flux and photosynthesis active radiation observations, and half-hourly environmental controls we measure include temperature, humidity, precipitation, radiation, tidal height, salinity, etc. Another aim is to explore the links between canopy LUE and spectral indices derived from near-surface tower-based remote sensing (normalized difference vegetation index, enhanced vegetation index, photochemical reflectance index, solar-induced chlorophyll fluorescence, etc.), and then identify potential quantitative relationships for developing remote sensing-based estimation methods of canopy LUE. At present, some instruments in our in-situ observation system have not yet been installed (planned in next months) and therefore we don't have enough measurements to support our analysis. However, a preliminary analysis of our historical EC and climate observations in past several years indicates that canopy LUE shows strong temporal variability and is greatly affected by environmental factors such as tidal activity. Detailed and systematic analyses of temporal variability of canopy LUE and its environmental controls and potential remote sensing estimation methods will be conducted when our in-situ observation system is ready in near future.

Evaluation of the Consistency among In Situ and Remote Sensing Measurements of CO2 over North America using the CarbonTracker-Lagrange Regional Inverse Modeling Framework

NASA Astrophysics Data System (ADS)

Andrews, A. E.; Trudeau, M.; Hu, L.; Thoning, K. W.; Shiga, Y. P.; Michalak, A. M.; Benmergui, J. S.; Mountain, M. E.; Nehrkorn, T.; O'Dell, C.; Jacobson, A. R.; Miller, J.; Sweeney, C.; Chen, H.; Ploeger, F.; Tans, P. P.

2017-12-01

CarbonTracker-Lagrange (CT-L) is a regional inverse modeling system for estimating CO2 fluxes with rigorous uncertainty quantification. CT-L uses footprints from the Stochastic Time-Inverted Lagrangian Transport (STILT) model driven by high-resolution (10 to 30 km) meteorological fields from the Weather Research and Forecasting (WRF) model. We have computed a library of footprints corresponding to in situ and remote sensing measurements of CO2 over North America for 2007-2015. GOSAT and OCO-2 XCO2 retrievals are simulated using a suite of CT-L terrestrial ecosystem flux estimates that have been optimized with respect to in situ atmospheric CO2 measurements along with fossil fuel fluxes from emissions inventories. A vertical profile of STILT-WRF footprints was constructed corresponding to each simulated satellite retrieval, and CO2 profiles are generated by convolving the footprints with fluxes and attaching initial values advected from the domain boundaries. The stratospheric contribution to XCO2 has been estimated using 4-dimensional CO2 fields from the NOAA CarbonTracker model (version CT2016) and from the Chemical Lagrangian Model of the Stratosphere (CLaMS), after scaling the model fields to match data from the NOAA AirCore surface-to-stratosphere air sampling system. Tropospheric lateral boundary conditions are from CT2016 and from an empirical boundary value product derived from aircraft and marine boundary layer data. The averaging kernel and a priori CO2 profile are taken into account for direct comparisons with retrievals. We have focused on North America due to the relatively dense in situ measurements available with the aim of developing strategies for combined assimilation of in situ and remote sensing data. We will consider the extent to which interannual variability in terrestrial fluxes is manifest in the real and simulated satellite retrievals, and we will investigate possible systematic biases in the satellite retrievals and in the model.

Upper-Tropospheric Winds Derived from Geostationary Satellite Water Vapor Observations

NASA Technical Reports Server (NTRS)

Velden, Christopher S.; Hayden, Christopher M.; Nieman, Steven J.; Menzel, W. Paul; Wanzong, Steven; Goerss, James S.

1997-01-01

The coverage and quality of remotely sensed upper-tropospheric moisture parameters have improved considerably with the deployment of a new generation of operational geostationary meteorological satellites: GOES-8/9 and GMS-5. The GOES-8/9 water vapor imaging capabilities have increased as a result of improved radiometric sensitivity and higher spatial resolution. The addition of a water vapor sensing channel on the latest GMS permits nearly global viewing of upper-tropospheric water vapor (when joined with GOES and Meteosat) and enhances the commonality of geostationary meteorological satellite observing capabilities. Upper-tropospheric motions derived from sequential water vapor imagery provided by these satellites can be objectively extracted by automated techniques. Wind fields can be deduced in both cloudy and cloud-free environments. In addition to the spatially coherent nature of these vector fields, the GOES-8/9 multispectral water vapor sensing capabilities allow for determination of wind fields over multiple tropospheric layers in cloud-free environments. This article provides an update on the latest efforts to extract water vapor motion displacements over meteorological scales ranging from subsynoptic to global. The potential applications of these data to impact operations, numerical assimilation and prediction, and research studies are discussed.

Geotechnical applications of remote sensing and remote data transmission; Proceedings of the Symposium, Cocoa Beach, FL, Jan. 31-Feb. 1, 1986

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, A.I.; Pettersson, C.B.

1988-01-01

Papers and discussions concerning the geotechnical applications of remote sensing and remote data transmission, sources of remotely sensed data, and glossaries of remote sensing and remote data transmission terms, acronyms, and abbreviations are presented. Aspects of remote sensing use covered include the significance of lineaments and their effects on ground-water systems, waste-site use and geotechnical characterization, the estimation of reservoir submerging losses using CIR aerial photographs, and satellite-based investigation of the significance of surficial deposits for surface mining operations. Other topics presented include the location of potential ground subsidence and collapse features in soluble carbonate rock, optical Fourier analysis ofmore » surface features of interest in geotechnical engineering, geotechnical applications of U.S. Government remote sensing programs, updating the data base for a Geographic Information System, the joint NASA/Geosat Test Case Project, the selection of remote data telemetry methods for geotechnical applications, the standardization of remote sensing data collection and transmission, and a comparison of airborne Goodyear electronic mapping system/SAR with satelliteborne Seasat/SAR radar imagery.« less

Education in Environmental Remote Sensing: Potentials and Problems.

ERIC Educational Resources Information Center

Kiefer, Ralph W.; Lillesand, Thomas M.

1983-01-01

Discusses remote sensing principles and applications and the status and needs of remote sensing education in the United States. A summary of the fundamental policy issues that will determine remote sensing's future role in environmental and resource managements is included. (Author/BC)

THE EPA REMOTE SENSING ARCHIVE

EPA Science Inventory

What would you do if you were faced with organizing 30 years of remote sensing projects that had been haphazardly stored at two separate locations for years then combined? The EPA Remote Sensing Archive, currently located in Las Vegas, Nevada. contains the remote sensing data and...

Research on remote sensing image pixel attribute data acquisition method in AutoCAD

NASA Astrophysics Data System (ADS)

Liu, Xiaoyang; Sun, Guangtong; Liu, Jun; Liu, Hui

2013-07-01

The remote sensing image has been widely used in AutoCAD, but AutoCAD lack of the function of remote sensing image processing. In the paper, ObjectARX was used for the secondary development tool, combined with the Image Engine SDK to realize remote sensing image pixel attribute data acquisition in AutoCAD, which provides critical technical support for AutoCAD environment remote sensing image processing algorithms.

Bibliography of Remote Sensing Techniques Used in Wetland Research.

DTIC Science & Technology

1993-01-01

remote sensing technology for detecting changes in wetland environments. This report documents a bibliographic search conducted as part of that work unit on applications of remote sensing techniques in wetland research. Results were used to guide research efforts on the use of remote sensing technology for wetland change detection and assessment. The citations are presented in three appendixes, organized by wetland type, sensor type, and author.... Change detection, Wetland assessment, Remote sensing ,

Ecological Assimilation of Land and Climate Observations - the EALCO model

NASA Astrophysics Data System (ADS)

Wang, S.; Zhang, Y.; Trishchenko, A.

2004-05-01

Ecosystems are intrinsically dynamic and interact with climate at a highly integrated level. Climate variables are the main driving factors in controlling the ecosystem physical, physiological, and biogeochemical processes including energy balance, water balance, photosynthesis, respiration, and nutrient cycling. On the other hand, ecosystems function as an integrity and feedback on the climate system through their control on surface radiation balance, energy partitioning, and greenhouse gases exchange. To improve our capability in climate change impact assessment, a comprehensive ecosystem model is required to address the many interactions between climate change and ecosystems. In addition, different ecosystems can have very different responses to the climate change and its variation. To provide more scientific support for ecosystem impact assessment at national scale, it is imperative that ecosystem models have the capability of assimilating the large scale geospatial information including satellite observations, GIS datasets, and climate model outputs or reanalysis. The EALCO model (Ecological Assimilation of Land and Climate Observations) is developed for such purposes. EALCO includes the comprehensive interactions among ecosystem processes and climate, and assimilates a variety of remote sensing products and GIS database. It provides both national and local scale model outputs for ecosystem responses to climate change including radiation and energy balances, water conditions and hydrological cycles, carbon sequestration and greenhouse gas exchange, and nutrient (N) cycling. These results form the foundation for the assessment of climate change impact on ecosystems, their services, and adaptation options. In this poster, the main algorithms for the radiation, energy, water, carbon, and nitrogen simulations were diagrammed. Sample input data layers at Canada national scale were illustrated. Model outputs including the Canada wide spatial distributions of net radiation, evapotranspiration, gross primary production, net primary production, and net ecosystem production were discussed.

Prediction and assimilation of surf-zone processes using a Bayesian network: Part II: Inverse models

USGS Publications Warehouse

Plant, Nathaniel G.; Holland, K. Todd

2011-01-01

A Bayesian network model has been developed to simulate a relatively simple problem of wave propagation in the surf zone (detailed in Part I). Here, we demonstrate that this Bayesian model can provide both inverse modeling and data-assimilation solutions for predicting offshore wave heights and depth estimates given limited wave-height and depth information from an onshore location. The inverse method is extended to allow data assimilation using observational inputs that are not compatible with deterministic solutions of the problem. These inputs include sand bar positions (instead of bathymetry) and estimates of the intensity of wave breaking (instead of wave-height observations). Our results indicate that wave breaking information is essential to reduce prediction errors. In many practical situations, this information could be provided from a shore-based observer or from remote-sensing systems. We show that various combinations of the assimilated inputs significantly reduce the uncertainty in the estimates of water depths and wave heights in the model domain. Application of the Bayesian network model to new field data demonstrated significant predictive skill (R2 = 0.7) for the inverse estimate of a month-long time series of offshore wave heights. The Bayesian inverse results include uncertainty estimates that were shown to be most accurate when given uncertainty in the inputs (e.g., depth and tuning parameters). Furthermore, the inverse modeling was extended to directly estimate tuning parameters associated with the underlying wave-process model. The inverse estimates of the model parameters not only showed an offshore wave height dependence consistent with results of previous studies but the uncertainty estimates of the tuning parameters also explain previously reported variations in the model parameters.

Kite Aerial Photography as a Tool for Remote Sensing

ERIC Educational Resources Information Center

Sallee, Jeff; Meier, Lesley R.

2010-01-01

As humans, we perform remote sensing nearly all the time. This is because we acquire most of our information about our surroundings through the senses of sight and hearing. Whether viewed by the unenhanced eye or a military satellite, remote sensing is observing objects from a distance. With our current technology, remote sensing has become a part…

A Multi-Institutional Big Data Collaboration to Estimate Long Term Terrestrial Net Carbon Uptake from Remote Sensing and Hydrological Modeling

NASA Astrophysics Data System (ADS)

Halem, M.; Dorband, J.; Rao, R.; Lomonaco, S.; Chapman, D. R.; LeMoigne, J.; Nearing, G. S.; Pelissier, C. S.; Simpson, D. G.; Clune, T.

2014-12-01

Recent aircraft measurements from scattered records have shown long-term, global, seasonal photosynthetic CO2 uptake over land accelerating over the past 50 years. The successful launch of the sun-synchronous Orbiting Carbon Observatory 2 (OCO-2) on July 2, 2014 is expected to provide global, high spatial and spectral resolution datasets of vertical CO2 concentrations with surface spectral resolutions capable of yielding accurate CO2 flux profiles. It is unclear whether the biosphere will continue to act as a sink for anthropogenic CO2 loading of the atmosphere. Since current climate models with detailed terrestrial ecosystems are unable to simulate the observed increase in net ecosystem production (NEP), we will conduct assimilation studies with the derived CO2 fluxes in the GSFC Land Information System hydrological model to validate the generated NEP uptake. Further, we plan to use the OCO-2 CO2 concentrations to train a neural network to enable the calculation of long term trends from a decade of AIRS CO2 concentration data to produce regional NEP. To address this important Big Data science issue, a multi-institutional collaboration was formed to leverage their combined resources and the expertise of the researchers at the NASA GSFC, the Lamont Doherty Earth Observatory and UMBC. We will employ a high speed 10Gb network to connect the collaborating researchers and provide them with remote access to dedicated computational hybrid multicore resources at UMBC, as well as access to an archive containing more than a decade of readily accessible continuous daily gridded AIRS data and ten years of daily MODIS data for each September. The status of the following research efforts is planned to be presented; (i) acquisition and processing of the expected CO2 profile data from OCO-2 for two test sites, a low latitude region over the Amazon and a Boral forest at high latitude, (ii) initial impact of 3-D data assimilation of CO2 fluxes with the advanced Goddard LIS hydrological surface model, (iii) preliminary results in training AIRS CO2 data. In addition, early results of innovative exploration on quantum annealing optimization for 3-D data assimilation, image registration and a Hopfield neural network for training the AIRS CO2 spectral data through UMBC remote access to the D-Wave system in Vancouver, CA, will be introduced.

Remote sensing for detecting and mapping whitefly (Bemisia tabaci) infestations

USDA-ARS?s Scientific Manuscript database

Remote sensing technology has long been used for detecting insect infestations on agricultural crops. With recent advances in remote sensing sensors and other spatial information technologies such as Global Position Systems (GPS) and Geographic Information Systems (GIS), remote sensing is finding mo...

Reflections on Earth--Remote-Sensing Research from Your Classroom.

ERIC Educational Resources Information Center

Campbell, Bruce A.

2001-01-01

Points out the uses of remote sensing in different areas, and introduces the program "Reflections on Earth" which provides access to basic and instructional information on remote sensing to students and teachers. Introduces students to concepts related to remote sensing and measuring distances. (YDS)

Remote-Sensing Practice and Potential

DTIC Science & Technology

1974-05-01

Six essential processes that must be accomplished if use of a remote - sensing system is to result in useful information are defined as problem...to be useful in remote - sensing projects are described. An overview of the current state-of-the-art of remote sensing is presented.

History and future of remote sensing technology and education

NASA Technical Reports Server (NTRS)

Colwell, R. N.

1980-01-01

A historical overview of the discovery and development of photography, related sciences, and remote sensing technology is presented. The role of education to date in the development of remote sensing is discussed. The probable future and potential of remote sensing and training is described.

Ten ways remote sensing can contribute to conservation

USGS Publications Warehouse

Rose, Robert A.; Byler, Dirck; Eastman, J. Ron; Fleishman, Erica; Geller, Gary; Goetz, Scott; Guild, Liane; Hamilton, Healy; Hansen, Matt; Headley, Rachel; Hewson, Jennifer; Horning, Ned; Kaplin, Beth A.; Laporte, Nadine; Leidner, Allison K.; Leimgruber, Peter; Morisette, Jeffrey T.; Musinsky, John; Pintea, Lilian; Prados, Ana; Radeloff, Volker C.; Rowen, Mary; Saatchi, Sassan; Schill, Steve; Tabor, Karyn; Turner, Woody; Vodacek, Anthony; Vogelmann, James; Wegmann, Martin; Wilkie, David; Wilson, Cara

2014-01-01

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners’ use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain-referral survey. We then used a workshop-based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real-time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing-derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions?

Ten ways remote sensing can contribute to conservation.

PubMed

Rose, Robert A; Byler, Dirck; Eastman, J Ron; Fleishman, Erica; Geller, Gary; Goetz, Scott; Guild, Liane; Hamilton, Healy; Hansen, Matt; Headley, Rachel; Hewson, Jennifer; Horning, Ned; Kaplin, Beth A; Laporte, Nadine; Leidner, Allison; Leimgruber, Peter; Morisette, Jeffrey; Musinsky, John; Pintea, Lilian; Prados, Ana; Radeloff, Volker C; Rowen, Mary; Saatchi, Sassan; Schill, Steve; Tabor, Karyn; Turner, Woody; Vodacek, Anthony; Vogelmann, James; Wegmann, Martin; Wilkie, David; Wilson, Cara

2015-04-01

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners' use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain-referral survey. We then used a workshop-based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real-time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing-derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions? © 2014 Society for Conservation Biology.

Role of remote sensing in documenting living resources

NASA Technical Reports Server (NTRS)

Wagner, P. E.; Anderson, R. R.; Brun, B.; Eisenberg, M.; Genys, J. B.; Lear, D. W., Jr.; Miller, M. H.

1978-01-01

Specific cases of known or potentially useful applications of remote sensing in assessing biological resources are discussed. It is concluded that the more usable remote sensing techniques relate to the measurement of population fluctuations in aquatic systems. Sensing of the flora and the fauna of the Bay is considered with emphasis on direct sensing of aquatic plant populations and of water quality. Recommendations for remote sensing projects are given.

Commercial future: making remote sensing a media event

NASA Astrophysics Data System (ADS)

Lurie, Ian

1999-12-01

The rapid growth of commercial remote sensing has made high quality digital sensing data widely available -- now, remote sensing must become and remain a strong, commercially viable industry. However, this new industry cannot survive without an educated consumer base. To access markets, remote sensing providers must make their product more accessible, both literally and figuratively: Potential customers must be able to find the data they require, when they require it, and they must understand the utility of the information available to them. The Internet and the World Wide Web offer the perfect medium to educate potential customers and to sell remote sensing data to those customers. A well-designed web presence can provide both an information center and a market place for companies offering their data for sale. A very high potential web-based market for remote sensing lies in media. News agencies, web sites, and a host of other visual media services can use remote sensing data to provide current, relevant information regarding news around the world. This paper will provide a model for promotion and sale of remote sensing data via the Internet.

77 FR 39220 - Advisory Committee on Commercial Remote Sensing (ACCRES); Charter Renewal

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-02

... Commercial Remote Sensing (ACCRES); Charter Renewal AGENCY: National Oceanic and Atmospheric Administration... Committee on Commercial Remote Sensing (ACCRES) was renewed on March 14, 2012. SUPPLEMENTARY INFORMATION: In... Commercial Remote Sensing (ACCRES) is in the public interest in connection with the performance of duties...

76 FR 66042 - Advisory Committee on Commercial Remote Sensing (ACCRES); Request for Nominations

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-25

... Commercial Remote Sensing (ACCRES); Request for Nominations ACTION: Notice requesting nominations for the Advisory Committee on Commercial Remote Sensing (ACCRES). SUMMARY: The Advisory Committee on Commercial Remote Sensing (ACCRES) was established to advise the Secretary of Commerce, through the Under Secretary...

An introduction to quantitative remote sensing. [data processing

NASA Technical Reports Server (NTRS)

Lindenlaub, J. C.; Russell, J.

1974-01-01

The quantitative approach to remote sensing is discussed along with the analysis of remote sensing data. Emphasis is placed on the application of pattern recognition in numerically oriented remote sensing systems. A common background and orientation for users of the LARS computer software system is provided.

Improved Assimilation of Streamflow and Satellite Soil Moisture with the Evolutionary Particle Filter and Geostatistical Modeling

NASA Astrophysics Data System (ADS)

Yan, Hongxiang; Moradkhani, Hamid; Abbaszadeh, Peyman

2017-04-01

Assimilation of satellite soil moisture and streamflow data into hydrologic models using has received increasing attention over the past few years. Currently, these observations are increasingly used to improve the model streamflow and soil moisture predictions. However, the performance of this land data assimilation (DA) system still suffers from two limitations: 1) satellite data scarcity and quality; and 2) particle weight degeneration. In order to overcome these two limitations, we propose two possible solutions in this study. First, the general Gaussian geostatistical approach is proposed to overcome the limitation in the space/time resolution of satellite soil moisture products thus improving their accuracy at uncovered/biased grid cells. Secondly, an evolutionary PF approach based on Genetic Algorithm (GA) and Markov Chain Monte Carlo (MCMC), the so-called EPF-MCMC, is developed to further reduce weight degeneration and improve the robustness of the land DA system. This study provides a detailed analysis of the joint and separate assimilation of streamflow and satellite soil moisture into a distributed Sacramento Soil Moisture Accounting (SAC-SMA) model, with the use of recently developed EPF-MCMC and the general Gaussian geostatistical approach. Performance is assessed over several basins in the USA selected from Model Parameter Estimation Experiment (MOPEX) and located in different climate regions. The results indicate that: 1) the general Gaussian approach can predict the soil moisture at uncovered grid cells within the expected satellite data quality threshold; 2) assimilation of satellite soil moisture inferred from the general Gaussian model can significantly improve the soil moisture predictions; and 3) in terms of both deterministic and probabilistic measures, the EPF-MCMC can achieve better streamflow predictions. These results recommend that the geostatistical model is a helpful tool to aid the remote sensing technique and the EPF-MCMC is a reliable and effective DA approach in hydrologic applications.

Data Assimilation of AirSWOT and Synthetically Derived SWOT Observations of Water Surface Elevation in a Multichannel River

NASA Astrophysics Data System (ADS)

Altenau, E. H.; Pavelsky, T.; Andreadis, K.; Bates, P. D.; Neal, J. C.

2017-12-01

Multichannel rivers continue to be challenging features to quantify, especially at regional and global scales, which is problematic because accurate representations of such environments are needed to properly monitor the earth's water cycle as it adjusts to climate change. It has been demonstrated that higher-complexity, 2D models outperform lower-complexity, 1D models in simulating multichannel river hydraulics at regional scales due to the inclusion of the channel network's connectivity. However, new remote sensing measurements from the future Surface Water and Ocean Topography (SWOT) mission and it's airborne analog AirSWOT offer new observations that can be used to try and improve the lower-complexity, 1D models to achieve accuracies closer to the higher-complexity, 2D codes. Here, we use an Ensemble Kalman Filter (EnKF) to assimilate AirSWOT water surface elevation (WSE) measurements from a 2015 field campaign into a 1D hydrodynamic model along a 90 km reach of Tanana River, AK. This work is the first to test data assimilation methods using real SWOT-like data from AirSWOT. Additionally, synthetic SWOT observations of WSE are generated across the same study site using a fine-resolution 2D model and assimilated into the coarser-resolution 1D model. Lastly, we compare the abilities of AirSWOT and the synthetic-SWOT observations to improve spatial and temporal model outputs in WSEs. Results indicate 1D model outputs of spatially distributed WSEs improve as observational coverage increases, and improvements in temporal fluctuations in WSEs depend on the number of observations. Furthermore, results reveal that assimilation of AirSWOT observations produce greater error reductions in 1D model outputs compared to synthetic SWOT observations due to lower measurement errors. Both AirSWOT and the synthetic SWOT observations significantly lower spatial and temporal errors in 1D model outputs of WSEs.

Numerical modelling of erosion and assimilation of sulfur-rich substrate by martian lava flows: Implications for the genesis of massive sulfide mineralization on Mars

NASA Astrophysics Data System (ADS)

Baumgartner, Raphael J.; Baratoux, David; Gaillard, Fabrice; Fiorentini, Marco L.

2017-11-01

Mantle-derived volcanic rocks on Mars display physical and chemical commonalities with mafic-ultramafic ferropicrite and komatiite volcanism on the Earth. Terrestrial komatiites are common hosts of massive sulfide mineralization enriched in siderophile-chalcophile precious metals (i.e., Ni, Cu, and the platinum-group elements). These deposits correspond to the batch segregation and accumulation of immiscible sulfide liquids as a consequence of mechanical/thermo-mechanical erosion and assimilation of sulfur-rich bedrock during the turbulent flow of high-temperature and low-viscosity komatiite lava flows. This study adopts this mineralization model and presents numerical simulations of erosion and assimilation of sulfide- and sulfate-rich sedimentary substrates during the dynamic emplacement of (channelled) mafic-ultramafic lava flows on Mars. For sedimentary substrates containing adequate sulfide proportions (e.g., 1 wt% S), our simulations suggest that sulfide supersaturation in low-temperature (< 1350 °C) flows could be attained at < 200 km distance, but may be postponed in high-temperature lavas flows (> 1400 °C). The precious-metals tenor in the derived immiscible sulfide liquids may be significantly upgraded as a result of their prolonged equilibration with large volumes of silicate melts along flow conduits. The influence of sulfate assimilation on sulfide supersaturation in martian lava flows is addressed by simulations of melt-gas equilibration in the Csbnd Hsbnd Osbnd S fluid system. However, prolonged sulfide segregation and deposit genesis by means of sulfate assimilation appears to be limited by lava oxidation and the release of sulfur-rich gas. The identification of massive sulfide endowments on Mars is not possible from remote sensing data. Yet the results of this study aid to define regions for the potential occurrence of such mineral systems, which may be the large canyon systems Noctis Labyrinthus and Valles Marineris, or the Hesperian channel systems of Mars' highlands (e.g., Kasei Valles), most of which have been periodically draped by mafic-ultramafic lavas.

Seasonal-scale Observational Data Analysis and Atmospheric Phenomenology for the Cold Land Processes Experiment

NASA Technical Reports Server (NTRS)

Poulos, Gregory S.; Stamus, Peter A.; Snook, John S.

2005-01-01

The Cold Land Processes Experiment (CLPX) experiment emphasized the development of a strong synergism between process-oriented understanding, land surface models and microwave remote sensing. Our work sought to investigate which topographically- generated atmospheric phenomena are most relevant to the CLPX MSA's for the purpose of evaluating their climatic importance to net local moisture fluxes and snow transport through the use of high-resolution data assimilation/atmospheric numerical modeling techniques. Our task was to create three long-term, scientific quality atmospheric datasets for quantitative analysis (for all CLPX researchers) and provide a summary of the meteorologically-relevant phenomena of the three MSAs (see Figure) over northern Colorado. Our efforts required the ingest of a variety of CLPX datasets and the execution an atmospheric and land surface data assimilation system based on the Navier-Stokes equations (the Local Analysis and Prediction System, LAPS, and an atmospheric numerical weather prediction model, as required) at topographically- relevant grid spacing (approx. 500 m). The resulting dataset will be analyzed by the CLPX community as a part of their larger research goals to determine the relative influence of various atmospheric phenomena on processes relevant to CLPX scientific goals.

Validation of reactive gases and aerosols in the MACC global analysis and forecast system

NASA Astrophysics Data System (ADS)

Eskes, H.; Huijnen, V.; Arola, A.; Benedictow, A.; Blechschmidt, A.-M.; Botek, E.; Boucher, O.; Bouarar, I.; Chabrillat, S.; Cuevas, E.; Engelen, R.; Flentje, H.; Gaudel, A.; Griesfeller, J.; Jones, L.; Kapsomenakis, J.; Katragkou, E.; Kinne, S.; Langerock, B.; Razinger, M.; Richter, A.; Schultz, M.; Schulz, M.; Sudarchikova, N.; Thouret, V.; Vrekoussis, M.; Wagner, A.; Zerefos, C.

2015-02-01

The European MACC (Monitoring Atmospheric Composition and Climate) project is preparing the operational Copernicus Atmosphere Monitoring Service (CAMS), one of the services of the European Copernicus Programme on Earth observation and environmental services. MACC uses data assimilation to combine in-situ and remote sensing observations with global and regional models of atmospheric reactive gases, aerosols and greenhouse gases, and is based on the Integrated Forecast System of the ECMWF. The global component of the MACC service has a dedicated validation activity to document the quality of the atmospheric composition products. In this paper we discuss the approach to validation that has been developed over the past three years. Topics discussed are the validation requirements, the operational aspects, the measurement data sets used, the structure of the validation reports, the models and assimilation systems validated, the procedure to introduce new upgrades, and the scoring methods. One specific target of the MACC system concerns forecasting special events with high pollution concentrations. Such events receive extra attention in the validation process. Finally, a summary is provided of the results from the validation of the latest set of daily global analysis and forecast products from the MACC system reported in November 2014.

Remote Sensing of Ecology, Biodiversity and Conservation: A Review from the Perspective of Remote Sensing Specialists

PubMed Central

Wang, Kai; Franklin, Steven E.; Guo, Xulin; Cattet, Marc

2010-01-01

Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC). Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS). PMID:22163432

Remote sensing of ecology, biodiversity and conservation: a review from the perspective of remote sensing specialists.

PubMed

Wang, Kai; Franklin, Steven E; Guo, Xulin; Cattet, Marc

2010-01-01

Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC). Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS).

Remote Sensing and Reflectance Profiling in Entomology.

PubMed

Nansen, Christian; Elliott, Norman

2016-01-01

Remote sensing describes the characterization of the status of objects and/or the classification of their identity based on a combination of spectral features extracted from reflectance or transmission profiles of radiometric energy. Remote sensing can be benchtop based, and therefore acquired at a high spatial resolution, or airborne at lower spatial resolution to cover large areas. Despite important challenges, airborne remote sensing technologies will undoubtedly be of major importance in optimized management of agricultural systems in the twenty-first century. Benchtop remote sensing applications are becoming important in insect systematics and in phenomics studies of insect behavior and physiology. This review highlights how remote sensing influences entomological research by enabling scientists to nondestructively monitor how individual insects respond to treatments and ambient conditions. Furthermore, novel remote sensing technologies are creating intriguing interdisciplinary bridges between entomology and disciplines such as informatics and electrical engineering.

Remote Sensing in Geography in the New Millennium: Prospects, Challenges, and Opportunities

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Jensen, John R.; Morain, Stanley A.; Walsh, Stephen J.; Ridd, Merrill K.

1999-01-01

Remote sensing science contributes greatly to our understanding of the Earth's ecosystems and cultural landscapes. Almost all the natural and social sciences, including geography, rely heavily on remote sensing to provide quantitative, and indispensable spatial information. Many geographers have made significant contributions to remote sensing science since the 1970s, including the specification of advanced remote sensing systems, improvements in analog and digital image analysis, biophysical modeling, and terrain analysis. In fact, the Remote Sensing Specialty Group (RSSG) is one of the largest specialty groups within the AAG with over 500 members. Remote sensing in concert with a geographic information systems, offers much value to geography as both an incisive spatial-analytical tool and as a scholarly pursuit that adds to the body of geographic knowledge on the whole. The "power" of remote sensing as a research endeavor in geography lies in its capabilities for obtaining synoptic, near-real time data at many spatial and temporal scales, and in many regions of the electromagnetic spectrum - from microwave, to RADAR, to visible, and reflective and thermal infrared. In turn, these data present a vast compendium of information for assessing Earth attributes and characte6stics that are at the very core of geography. Here we revisit how remote sensing has become a fundamental and important tool for geographical research, and how with the advent of new and improved sensing systems to be launched in the near future, remote sensing will further advance geographical analysis in the approaching New Millennium.

Agricultural Production Monitoring in the Sahel Using Remote Sensing: Present Possibilities and Research Needs

DTIC Science & Technology

1993-01-01

during the agricultural season. Satellite remote sensing can contribute significantly to such a system by collecting information on crops and on...well as techniques to derive biophysical variables from remotely-sensed data. Finally, the integration of these remote - sensing techniques with crop

Four dimensional variational assimilation of in-situ and remote-sensing aerosol data

NASA Astrophysics Data System (ADS)

Nieradzik, L. P.; Elbern, H.

2012-04-01

Aerosols play an increasingly important role in atmospheric modelling. They have a strong influence on the radiative transfer balance and a significant impact on human health. Their origin is various and so are its effects. Most of the measurement sites in Europe account for an integrated aerosol load PMx (Particulate Matter of less than x μm in diameter) which does not give any qualitative information on the composition of the aerosol. Since very different constituents contribute to PMx, like e.g. mineral dust derived from desert storms or sea salt, it is necessary to make aerosol forecasts not only of load, but also type resolved. The method of four dimensional variational data assimilation (4Dvar) is a widely known technique to enhance forecast skills of CTMs (Chemistry-Transport-Models) by ingesting in-situ and, especially, remote-sensing measurements. The EURAD-IM (EURopean Air pollution Dispersion - Inverse Model), containing a full adjoint gas-phase model, has been expanded with an adjoint of the MADE (Modal Aerosol Dynamics model for Europe) to optimise initial and boundary values for aerosols using 4Dvar. A forward and an adjoint radiative transfer model is driven by the EURAD-IM as mapping between BLAOT (Boundary Layer Aerosol Optical Thickness) and internal aerosol species. Furthermore, its condensation scheme has been bypassed by an HDMR (High-Dimensional-Model-Representation) to ensure differentiability. In this study both in-situ measured PMx as well as satellite retrieved aerosol optical thicknesses have been assimilated and the effect on forecast performance has been investigated. The source of BLAOT is the aerosol retrieval system SYNAER (SYNergetic AErosol Retrieval) from DLR-DFD that retrieves AOT by making use of both AATSR/SCIAMACHY and AVHRR/GOME-2 data respectively. Its strengths are a large spatial coverage, near real-time availability, and the classification of five intrinsic aerosol species, namely water-solubles, water-insolubles, soot, sea salt, and mineral dust which are furthermore size resolved in terms of modes. The skill of the aerosol 4Dvar system was tested in two episodes: 1) July through August 2003, a dry period with strong wildfire activity in Europe, and 2) October through November 2008, the period of the ZEPTER-2 (Second ZEPpelin based Tropospheric photochemical chemistry expERiment) measurement campaign in the area of Lake Constance. In the latter case one-way nesting has been applied from a horizontal grid resolution of 45 km down to 5 km. Overall, the results showed a significant increase in forecast quality of tropospheric aerosol loads.

Utility of ERTS for monitoring the breeding habitat of migratory waterfowl

NASA Technical Reports Server (NTRS)

Work, E. A., Jr.; Gilmer, D. S.; Klett, A. T.

1974-01-01

Since 1968 the Bureau of Sport Fisheries and Wildlife (BSF&W) and the Environmental Research Institute of Michigan have cooperated on developing applications of remote sensing to the management of migratory waterfowl. Basically, this work has been concerned with (1) the assimilation of data on surface water conditions so that the data can be used as an index of annual waterfowl production, and (2) the collection of data on land use and wetland quality so that a measure of habitat carrying capacity is obtained. To date, efforts have been directed toward utilizing ERTS to monitor surface water conditions. An example of a model used for predicting the annual production of mallards (Anas platyrhynchos) is presented. The data inputs to this model and the potential for acquiring these data using ERTS are described.

Assimilation of Sentinel-1 estimates of Precipitable Water Vapor (PWV) into a Numerical Weather Model for a more accurate forecast of extreme weather events

NASA Astrophysics Data System (ADS)

Mateus, Pedro; Nico, Giovanni; Catalao, Joao

2017-04-01

In the last two decades, SAR interferometry has been used to obtain maps of Precipitable Water Vapor (PWV).This maps are characterized by their high spatial resolution when compared to the currently available PWV measurements (e.g. GNSS, radiometers or radiosondes). Several previous works have shown that assimilating PWV values, mainly derived from GNSS observations, into Numerical Weather Models (NWMs) can significantly improve rainfall predictions.It is noteworthy that the PWV-derived from GNSS observations have a high temporal resolution but a low spatialone. In addition, there are many regions without any GNSS stations, where temporal and spatial distribution of PWV areonly available through satellite measurements. The first attempt to assimilate InSAR-derived maps of PWV (InSAR-PWV) into a NWM was made by Pichelli et al. [1].They used InSAR-PWV maps obtained from ENVISAT-ASAR images and the mesoscale weather prediction model MM5 over the city of Rome, Italy. The statistical indices show that the InSAR-PWVdata assimilation improves the forecast of weak to moderateprecipitation (< 15 mm/3-h). The second and last attempt, was performed by Mateus et al. [2]. They used the same satellite mission and the Weather Research and Forecast (WRF) model over the city of Lisbon, Portugal, during a light rain event not forecast by the model.Results showed that after data assimilation, there is a bias correction of the PWV field and an improvement in the forecast of the weakto moderate rainfall up to 9 h after the assimilation time. We used, for the first time, the Weather Research and Forecast Data Assimilation (WRFDA) model, at micro-scale resolutions (3 km), over the Iberian Peninsula (focusing on the southern region of Spain) and during a convective cell associated with a local heavy rainfall event, to study the impact of assimilation PWV maps obtained from SAR interferometric phase calculated using images acquired by the Sentinel-1 satellite. It's worth noting that, in this case, the model without assimilation PWV maps fails to reproduce the amount and the region of heavy rainfall. The assimilation of InSAR-PWV maps with high spatial variability by the WRF model, promoted alterations in the buoyancy force over the study area and consequently increased the atmospheric instability, were new convection cells were generated over the correct area. We assessed the results using in-situ meteorological data and with a meteorological radar. With the Sentinel-1 A/B C-band sensors its possible generate maps of PWV over large areas with a length of hundreds of kilometers and a width of about 250 km (country-spanning areas), a spatial resolution of 5×20 m and an absolute revisiting time of 6 days or fewer when combined with other sensors, opening new perspectives to the application of SAR meteorology concept. The availability of interferometric PWV maps on a routine basis can help to capture the high variability of the water vapor distribution at micro-scales. In this study, we show that the knowledge of the PWV with high spatial resolution can change the system thermodynamics to improve the NWP accuracy. Publication supported by the Short-Term Mobility Consiglio Nazionale delle Ricerche programs 2015 and 2016 and the Postdoctoral Grant SFRH/BPD/96069/2013. References: [1] E. Pichelli et al., "InSAR water vapor data assimilation into mesoscale model MM5: Technique and pilot study," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 8, no. 8, pp. 3859-3875, Aug. 2015. [2] P. Mateus, R. Tomé, G. Nico, and J. Catalão, "Three-Dimensional Variational Assimilation of InSAR PWV Using the WRFDA Model," IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 12, pp. 7323-7330, 2016.

Comparison of global sst analyses for atmospheric data assimilation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Phoebus, P.A.; Cummings, J.A.

1995-03-17

Traditionally, atmospheric models were executed using a climatological estimate of the sea surface temperature (SST) to define the marine boundary layer. More recently, particularly since the deployment of remote sensing instruments and the advent of multichannel SST observations atmospheric models have been improved by using more timely estimates of the actual state of the ocean. Typically, some type of objective analysis is performed using the data from satellites along with ship, buoy, and bathythermograph observations, and perhaps even climatology, to produce a weekly or daily analysis of global SST. Some of the earlier efforts to produce real-time global temperature analysesmore » have been described by Clancy and Pollak (1983) and Reynolds (1988). However, just as new techniques have been developed for atmospheric data assimilation, improvements have been made to ocean data assimilation systems as well. In 1988, the U.S. Navy`s Fleet Numerical Meteorology and Oceanography Center (FNMOC) implemented a global three-dimensional ocean temperature analysis that was based on the optimum interpolation methodology (Clancy et al., 1990). This system, the Optimum Thermal Interpolation System (OTIS 1.0), was initially distributed on a 2.50 resolution grid, and was later modified to generate fields on a 1.250 grid (OTIS 1.1; Clancy et al., 1992). Other optimum interpolation-based analyses (OTIS 3.0) were developed by FNMOC to perform high-resolution three-dimensional ocean thermal analyses in areas with strong frontal gradients and clearly defined water mass characteristics.« less

A Real-Time California Coastal Ocean Nowcast/Forecast System: Skill Assessment, User Products, and Transition from Research to Operations

NASA Astrophysics Data System (ADS)

Farrara, J. D.; Chao, Y.; Chai, F.; Zhang, H.

2016-02-01

The real-time California coastal ocean nowcast/forecast system is described. The model is based on the Regional Ocean Modeling System (ROMS) and covers the entire California coastal ocean with a horizontal resolution of 3 km and 40 vertical layers. The atmospheric forcing is derived from the operational regional atmospheric model forecasts. The lateral boundary conditions are provided by the operational ocean model forecasts. A multi-scale 3-dimensional variational (3DVAR) data assimilation scheme is used to assimilate both in situ (e.g., vertical profiles of temperature and salinity) and remotely sensed data from both satellite (e.g., sea surface temperature and sea surface height) and land-based platforms (e.g., surface current). The performance of our nowcast/forecast system is evaluated in real-time by a number of metrics that are published as soon as they become available. User tools and products have been developed for both general users and super-users (e.g., NOAA Office of Response and Restoration and USCG). Recent results comparing the 3DVAR with the ensemble Kalman Filter (EnKF) using Data Assimilation Research Testbed (DART) will be presented. Preliminary results coupling the ROMS circulation model with a biogeochemistry/ecosystem model (i.e., CoSiNE) will also discussed. Cloud computing services (e.g., Microsoft, Google) are now being tested to increase the reliability and timeliness in order to be accepted as a truly operational system in the near future.

Method of determining forest production from remotely sensed forest parameters

DOEpatents

Corey, J.C.; Mackey, H.E. Jr.

1987-08-31

A method of determining forest production entirely from remotely sensed data in which remotely sensed multispectral scanner (MSS) data on forest 5 composition is combined with remotely sensed radar imaging data on forest stand biophysical parameters to provide a measure of forest production. A high correlation has been found to exist between the remotely sensed radar imaging data and on site measurements of biophysical 10 parameters such as stand height, diameter at breast height, total tree height, mean area per tree, and timber stand volume.

Removal of Surface-Reflected Light for the Measurement of Remote-Sensing Reflectance from an Above-Surface Platform

DTIC Science & Technology

2010-12-01

remote - sensing reflectance) can be highly inaccurate if a spectrally constant value is applied (although errors can be reduced by carefully filtering measured raw data). To remove surface-reflected light in field measurements of remote sensing reflectance, a spectral optimization approach was applied, with results compared with those from remote sensing models and from direct measurements. The agreement from different determinations suggests that reasonable results for remote sensing reflectance of clear

Removal of Surface-Reflected Light for the Measurement of Remote-Sensing Reflectance from an Above-Surface Platform

DTIC Science & Technology

2010-12-06

remote - sensing reflectance) can be highly inaccurate if a spectrally constant value is applied (although errors can be reduced by carefully filtering measured raw data). To remove surface-reflected light in field measurements of remote sensing reflectance, a spectral optimization approach was applied, with results compared with those from remote sensing models and from direct measurements. The agreement from different determinations suggests that reasonable results for remote sensing reflectance of clear

Field Data Collection: an Essential Element in Remote Sensing Applications

NASA Technical Reports Server (NTRS)

Pettinger, L. R.

1971-01-01

Field data collected in support of remote sensing projects are generally used for the following purposes: (1) calibration of remote sensing systems, (2) evaluation of experimental applications of remote sensing imagery on small test sites, and (3) designing and evaluating operational regional resource studies and inventories which are conducted using the remote sensing imagery obtained. Field data may be used to help develop a technique for a particular application, or to aid in the application of that technique to a resource evaluation or inventory problem for a large area. Scientists at the Forestry Remote Sensing Laboratory have utilized field data for both purposes. How meaningful field data has been collected in each case is discussed.

Remote sensing and eLearning 2.0 for school education

NASA Astrophysics Data System (ADS)

Voss, Kerstin; Goetzke, Roland; Hodam, Henryk

2010-10-01

The "Remote Sensing in Schools" project aims at improving the integration of "Satellite remote sensing" into school teaching. Therefore, it is the project's overall objective to teach students in primary and secondary schools the basics and fields of application of remote sensing. Existing results show that many teachers are interested in remote sensing and at same time motivated to integrate it into their teaching. Despite the good intention, in the end, the implementation often fails due to the complexity and poor set-up of the information provided. Therefore, a comprehensive and well-structured learning platform on the topic of remote sensing is developed. The platform shall allow a structured introduction to the topic.

Remote sensing programs and courses in engineering and water resources

NASA Technical Reports Server (NTRS)

Kiefer, R. W.

1981-01-01

The content of typical basic and advanced remote sensing and image interpretation courses are described and typical remote sensing graduate programs of study in civil engineering and in interdisciplinary environmental remote sensing and water resources management programs are outlined. Ideally, graduate programs with an emphasis on remote sensing and image interpretation should be built around a core of five courses: (1) a basic course in fundamentals of remote sensing upon which the more specialized advanced remote sensing courses can build; (2) a course dealing with visual image interpretation; (3) a course dealing with quantitative (computer-based) image interpretation; (4) a basic photogrammetry course; and (5) a basic surveying course. These five courses comprise up to one-half of the course work required for the M.S. degree. The nature of other course work and thesis requirements vary greatly, depending on the department in which the degree is being awarded.

Remote sensing research in geographic education: An alternative view

NASA Technical Reports Server (NTRS)

Wilson, H.; Cary, T. K.; Goward, S. N.

1981-01-01

It is noted that within many geography departments remote sensing is viewed as a mere technique a student should learn in order to carry out true geographic research. This view inhibits both students and faculty from investigation of remotely sensed data as a new source of geographic knowledge that may alter our understanding of the Earth. The tendency is for geographers to accept these new data and analysis techniques from engineers and mathematicians without questioning the accompanying premises. This black-box approach hinders geographic applications of the new remotely sensed data and limits the geographer's contribution to further development of remote sensing observation systems. It is suggested that geographers contribute to the development of remote sensing through pursuit of basic research. This research can be encouraged, particularly among students, by demonstrating the links between geographic theory and remotely sensed observations, encouraging a healthy skepticism concerning the current understanding of these data.

Research on assessment and improvement method of remote sensing image reconstruction

NASA Astrophysics Data System (ADS)

Sun, Li; Hua, Nian; Yu, Yanbo; Zhao, Zhanping

2018-01-01

Remote sensing image quality assessment and improvement is an important part of image processing. Generally, the use of compressive sampling theory in remote sensing imaging system can compress images while sampling which can improve efficiency. A method of two-dimensional principal component analysis (2DPCA) is proposed to reconstruct the remote sensing image to improve the quality of the compressed image in this paper, which contain the useful information of image and can restrain the noise. Then, remote sensing image quality influence factors are analyzed, and the evaluation parameters for quantitative evaluation are introduced. On this basis, the quality of the reconstructed images is evaluated and the different factors influence on the reconstruction is analyzed, providing meaningful referential data for enhancing the quality of remote sensing images. The experiment results show that evaluation results fit human visual feature, and the method proposed have good application value in the field of remote sensing image processing.

Unmanned aerial vehicle: A unique platform for low-altitude remote sensing for crop management

USDA-ARS?s Scientific Manuscript database

Unmanned aerial vehicles (UAV) provide a unique platform for remote sensing to monitor crop fields that complements remote sensing from satellite, aircraft and ground-based platforms. The UAV-based remote sensing is versatile at ultra-low altitude to be able to provide an ultra-high-resolution imag...

Application of the remote-sensing communication model to a time-sensitive wildfire remote-sensing system

Treesearch

Christopher D. Lippitt; Douglas A. Stow; Philip J. Riggan

2016-01-01

Remote sensing for hazard response requires a priori identification of sensor, transmission, processing, and distribution methods to permit the extraction of relevant information in timescales sufficient to allow managers to make a given time-sensitive decision. This study applies and demonstrates the utility of the Remote Sensing Communication...

75 FR 32360 - Proposed Information Collection; Comment Request; Licensing of Private Remote-Sensing Space Systems

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-08

... Collection; Comment Request; Licensing of Private Remote-Sensing Space Systems AGENCY: National Oceanic and.... Abstract NOAA has established requirements for the licensing of private operators of remote-sensing space... Land Remote- Sensing Policy Act of 1992 and with the national security and international obligations of...

78 FR 44536 - Proposed Information Collection; Comment Request; Licensing of Private Remote-Sensing Space Systems

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-24

... Collection; Comment Request; Licensing of Private Remote-Sensing Space Systems AGENCY: National Oceanic and... for the licensing of private operators of remote-sensing space systems. The information in applications and subsequent reports is needed to ensure compliance with the Land Remote- Sensing Policy Act of...

Advancement of China’s Visible Light Remote Sensing Technology In Aerospace,

DTIC Science & Technology

1996-03-19

Aerospace visible light film systems were among the earliest space remote sensing systems to be developed in China. They have been applied very well...makes China the third nation in the world to master space remote sensing technology, it also puts recoverable remote sensing satellites among the first

Assimilation of passive and active CCI soil moisture products into hydrological modelling: an intercomparison study in Europe

NASA Astrophysics Data System (ADS)

Maggioni, V.; Massari, C.; Camici, S.; Brocca, L.; Marchesini, I.

2017-12-01

Soil moisture (SM) is a key variable in rainfall-runoff partitioning since it acts on the main hydrological processes taking part within a catchment. Modeling SM is often a difficult task due to its large variability at different temporal and spatial scales. Ground soil moisture measurements are a valuable tool for improving runoff prediction but are often limited and suffer from spatial representativeness issues. Remotely sensed observations offer a new source of data able to cope the latter issues thus opening new possibilities for improving flood simulations worldwide. Today, several different SM products are available at increased accuracy with respect to the past. Some interesting products are those derived from the Climate Change Initiative (CCI) which offer the most complete and most consistent global SM data record based on active and passive microwave sensors.Thanks to the combination of multiple sensors within an active, a passive and an active+passive products, the CCI SM is expected to provide a significant benefit for the improvement of rainfall-runoff simulations through data assimilation. However, previous studies have shown that the success of the assimilation is not only related to the accuracy of the observations but also to the specific climate and the catchment physical and hydrological characteristics as well as to many necessary choices related to the assimilation technique. These choices along with the type of SM observations (i.e. passive or active) might play an important role for the success or the failure of the assimilation exercise which is not still clear. In this study, based on a large dataset of catchments covering large part of the Europe, we assimilated satellite SM observations from the passive and the active CCI SM products into Modello Idrologico Semiditribuito in Continuo (MISDc, Brocca et al. 2011). Rainfall and temperature data were collected from the European Climate Assessment & Dataset (E-OBS) while discharge data were obtained from the Global Runoff Data Centre (GRDC). Preliminary results show a general improvement of the hydrological simulations for catchments located in the Mediterranean areas specifically for the active product while lower performance is obtained at northern latitudes due to the presence of snow and ice.

Polarimetric passive remote sensing of periodic surfaces

NASA Technical Reports Server (NTRS)

Veysoglu, Murat E.; Yueh, H. A.; Shin, R. T.; Kong, J. A.

1991-01-01

The concept of polarimetry in active remote sensing is extended to passive remote sensing. The potential use of the third and fourth Stokes parameters U and V, which play an important role in polarimetric active remote sensing, is demonstrated for passive remote sensing. It is shown that, by the use of the reciprocity principle, the polarimetric parameters of passive remote sensing can be obtained through the solution of the associated direct scattering problem. These ideas are applied to study polarimetric passive remote sensing of periodic surfaces. The solution of the direct scattering problem is obtained by an integral equation formulation which involves evaluation of periodic Green's functions and normal derivative of those on the surface. Rapid evaluation of the slowly convergent series associated with these functions is observed to be critical for the feasibility of the method. New formulas, which are rapidly convergent, are derived for the calculation of these series. The study has shown that the brightness temperature of the Stokes parameter U can be significant in passive remote sensing. Values as high as 50 K are observed for certain configurations.

From planets to crops and back: Remote sensing makes sense

NASA Astrophysics Data System (ADS)

Mustard, John F.

2017-04-01

Remotely sensed data and the instruments that acquire them are core parts of Earth and planetary observation systems. They are used to quantify the Earth's interconnected systems, and remote sensing is the only way to get a daily, or more frequent, snapshot of the status of the Earth. It really is the Earth's stethoscope. In a similar manner remote sensing is the rock hammer of the planetary scientist and the only way comprehensive data sets can be acquired. To risk offending many remotely sensed data acquired across the electromagnetic spectrum, it is the tricorder to explore known and unknown planets. Arriving where we are today in the use of remotely sensed data in the solar system has been a continually evolving synergy between Earth observation, planetary exploration, and fundamental laboratory work.

Remote sensing of on-road vehicle emissions: Mechanism, applications and a case study from Hong Kong

NASA Astrophysics Data System (ADS)

Huang, Yuhan; Organ, Bruce; Zhou, John L.; Surawski, Nic C.; Hong, Guang; Chan, Edward F. C.; Yam, Yat Shing

2018-06-01

Vehicle emissions are a major contributor to air pollution in cities and have serious health impacts to their inhabitants. On-road remote sensing is an effective and economic tool to monitor and control vehicle emissions. In this review, the mechanism, accuracy, advantages and limitations of remote sensing were introduced. Then the applications and major findings of remote sensing were critically reviewed. It was revealed that the emission distribution of on-road vehicles was highly skewed so that the dirtiest 10% vehicles accounted for over half of the total fleet emissions. Such findings highlighted the importance and effectiveness of using remote sensing for in situ identification of high-emitting vehicles for further inspection and maintenance programs. However, the accuracy and number of vehicles affected by screening programs were greatly dependent on the screening criteria. Remote sensing studies showed that the emissions of gasoline and diesel vehicles were significantly reduced in recent years, with the exception of NOx emissions of diesel vehicles in spite of greatly tightened automotive emission regulations. Thirdly, the experience and issues of using remote sensing for identifying high-emitting vehicles in Hong Kong (where remote sensing is a legislative instrument for enforcement purposes) were reported. That was followed by the first time ever identification and discussion of the issue of frequent false detection of diesel high-emitters using remote sensing. Finally, the challenges and future research directions of on-road remote sensing were elaborated.

Multivariate and Multiscale Data Assimilation in Terrestrial Systems: A Review

PubMed Central

Montzka, Carsten; Pauwels, Valentijn R. N.; Franssen, Harrie-Jan Hendricks; Han, Xujun; Vereecken, Harry

2012-01-01

More and more terrestrial observational networks are being established to monitor climatic, hydrological and land-use changes in different regions of the World. In these networks, time series of states and fluxes are recorded in an automated manner, often with a high temporal resolution. These data are important for the understanding of water, energy, and/or matter fluxes, as well as their biological and physical drivers and interactions with and within the terrestrial system. Similarly, the number and accuracy of variables, which can be observed by spaceborne sensors, are increasing. Data assimilation (DA) methods utilize these observations in terrestrial models in order to increase process knowledge as well as to improve forecasts for the system being studied. The widely implemented automation in observing environmental states and fluxes makes an operational computation more and more feasible, and it opens the perspective of short-time forecasts of the state of terrestrial systems. In this paper, we review the state of the art with respect to DA focusing on the joint assimilation of observational data precedents from different spatial scales and different data types. An introduction is given to different DA methods, such as the Ensemble Kalman Filter (EnKF), Particle Filter (PF) and variational methods (3/4D-VAR). In this review, we distinguish between four major DA approaches: (1) univariate single-scale DA (UVSS), which is the approach used in the majority of published DA applications, (2) univariate multiscale DA (UVMS) referring to a methodology which acknowledges that at least some of the assimilated data are measured at a different scale than the computational grid scale, (3) multivariate single-scale DA (MVSS) dealing with the assimilation of at least two different data types, and (4) combined multivariate multiscale DA (MVMS). Finally, we conclude with a discussion on the advantages and disadvantages of the assimilation of multiple data types in a simulation model. Existing approaches can be used to simultaneously update several model states and model parameters if applicable. In other words, the basic principles for multivariate data assimilation are already available. We argue that a better understanding of the measurement errors for different observation types, improved estimates of observation bias and improved multiscale assimilation methods for data which scale nonlinearly is important to properly weight them in multiscale multivariate data assimilation. In this context, improved cross-validation of different data types, and increased ground truth verification of remote sensing products are required. PMID:23443380

Multivariate and multiscale data assimilation in terrestrial systems: a review.

PubMed

Montzka, Carsten; Pauwels, Valentijn R N; Franssen, Harrie-Jan Hendricks; Han, Xujun; Vereecken, Harry

2012-11-26

More and more terrestrial observational networks are being established to monitor climatic, hydrological and land-use changes in different regions of the World. In these networks, time series of states and fluxes are recorded in an automated manner, often with a high temporal resolution. These data are important for the understanding of water, energy, and/or matter fluxes, as well as their biological and physical drivers and interactions with and within the terrestrial system. Similarly, the number and accuracy of variables, which can be observed by spaceborne sensors, are increasing. Data assimilation (DA) methods utilize these observations in terrestrial models in order to increase process knowledge as well as to improve forecasts for the system being studied. The widely implemented automation in observing environmental states and fluxes makes an operational computation more and more feasible, and it opens the perspective of short-time forecasts of the state of terrestrial systems. In this paper, we review the state of the art with respect to DA focusing on the joint assimilation of observational data precedents from different spatial scales and different data types. An introduction is given to different DA methods, such as the Ensemble Kalman Filter (EnKF), Particle Filter (PF) and variational methods (3/4D-VAR). In this review, we distinguish between four major DA approaches: (1) univariate single-scale DA (UVSS), which is the approach used in the majority of published DA applications, (2) univariate multiscale DA (UVMS) referring to a methodology which acknowledges that at least some of the assimilated data are measured at a different scale than the computational grid scale, (3) multivariate single-scale DA (MVSS) dealing with the assimilation of at least two different data types, and (4) combined multivariate multiscale DA (MVMS). Finally, we conclude with a discussion on the advantages and disadvantages of the assimilation of multiple data types in a simulation model. Existing approaches can be used to simultaneously update several model states and model parameters if applicable. In other words, the basic principles for multivariate data assimilation are already available. We argue that a better understanding of the measurement errors for different observation types, improved estimates of observation bias and improved multiscale assimilation methods for data which scale nonlinearly is important to properly weight them in multiscale multivariate data assimilation. In this context, improved cross-validation of different data types, and increased ground truth verification of remote sensing products are required.

Assimilation of SMOS brightness temperatures in the ECMWF EKF for the analysis of soil moisture

NASA Astrophysics Data System (ADS)

Munoz-Sabater, Joaquin

2012-07-01

Since November 2nd 2009, the European Centre for Medium-Range Weather Forecasts (ECMWF) has being monitoring, in Near Real Time (NRT), L-band brightness temperatures measured by the Soil Moisture and Ocean Salinity (SMOS) satellite mission of the European Space Agency (ESA). The main objective of the monitoring suite for SMOS data is to systematically monitor the difference between SMOS observed brightness temperatures and the corresponding model equivalent simulated by the Community Microwave Emission Model (CMEM), the so-called first guess departures. This is a crucial step, as first guess departures is the quantity used in the analysis. The ultimate goal is to investigate how the assimilation of SMOS brightness temperatures over land improves the weather forecast skill, through a more accurate initialization of the global soil moisture state. In this presentation, some significant results from the activities preparing for the assimilation of SMOS data are shown. Among these activities, an effective data thinning strategy, a practical approach to reduce noise from the observed brightness temperatures and a bias correction scheme are of special interest. Firstly, SMOS data needs to be significantly thinned as the data volume delivered for a single orbit is too large for the current operational capabilities in any Numerical Weather Prediction system. Different thinning strategies have been analysed and tested. The most suitable one is the assimilation of SMOS brightness temperatures which match the ECMWF T511 (~40 km) reduced Gaussian Grid. Secondly, SMOS observational noise is reduced significantly by averaging the data in angular bins. In addition, this methodology contributes to further thinning of the SMOS data before the analysis. Finally, a bias correction scheme based on a CDF matching is applied to the observations to ensure an unbiased dataset ready for assimilation in the ECMWF surface analysis system. The current ECMWF operational soil moisture analysis system is based on a point-wise Extended Kalman Filter (EKF). This system assimilates proxy surface observations, i.e., 2 m air temperature and relative humidity to analyse the soil moisture state. Recent developments have also made it possible to assimilate remote sensing data coming from active and passive instruments. In particular, the ECMWF EKF can also assimilate data from the Advanced Scatterometer (ASCAT) onboard METOP-A and, more recently, from SMOS brightness temperatures observations. The first preliminary assimilation results will be shown. The analysis fields will be evaluated through comparison to in-situ data from different regions.

Remote sensing of natural resources: Quarterly literature review

NASA Technical Reports Server (NTRS)

1976-01-01

A quarterly review of technical literature concerning remote sensing techniques is presented. The format contains indexed and abstracted materials with emphasis on data gathering techniques performed or obtained remotely from space, aircraft, or ground-based stations. Remote sensor applications including the remote sensing of natural resources are presented.

The DRAGON scale concept and results for remote sensing of aerosol properties

NASA Astrophysics Data System (ADS)

Holben, B. N.; Eck, T. F.; Schafer, J.; Giles, D. M.; Kim, J.; Sano, I.; Mukai, S.; Kim, Y. J.; Reid, J. S.; Pickering, K. E.; Crawford, J. H.; Smirnov, A.; Sinyuk, A.; Slutsker, I.; Sorokin, M.; Rodriguez, J.; Liew, S.; Trevino, N.; Lim, H.; Lefer, B. L.; Nadkarni, R.; Macke, A.; Kinne, S. A.; Anderson, B. E.; Russell, P. B.; Maring, H. B.; Welton, E. J.; da Silva, A.; Toon, O. B.; Redemann, J.

2013-12-01

Aerosol processes occur at microscales but are typically observed and reported at continental to global scales. Often observable aerosol processes that have significant anthropogenic impact occur on spatial scales of tens to a few hundred km, representative of convective cloud processing, urban/megacity sources, anthropogenic burning and natural wildfires, dry lakebed dust sources etc. Historically remote sensing of aerosols has relied on relatively coarse temporal and spatial resolution satellite observations or high temporal resolution point observations from ground-based monitoring sites from networks such as AERONET, SKYNET, MPLNET and many other surface observation platforms. Airborne remote and in situ observations combined with assimilation models were/are to be the mesoscale link between the ground- and space-based RS scales. However clearly the in situ and ground-based RS characterizations of aerosols require a convergence of thought, parameterization and actual scale measurements in order to advance this goal. This has been served by periodic multidisciplinary field campaigns yet only recently has a concerted effort been made to establish these ground-based networks in an effort to capture the mesoscale processes through measurement programs such as DISCOVER AQ and NASA AERONET's effort to foster such measurements and analysis through the Distributed Regional Aerosol Gridded Observation Networks (DRAGON), short term meso-networks, with partners in Asia and Europe and N. America. This talk will review the historical need for such networks and discuss some of the results and in some cases unexpected findings from the eight DRAGON campaigns conducted the last several years. Emphasis will be placed on the most recent DISCOVER AQ campaign conducted in Houston TX and the synergism with a regional to global network plan through the SEAC4RS US campaign.

Time Series Remote Sensing in Monitoring the Spatio-Temporal Dynamics of Plant Invasions: A Study of Invasive Saltcedar (Tamarix Spp.)

NASA Astrophysics Data System (ADS)

Diao, Chunyuan

In today's big data era, the increasing availability of satellite and airborne platforms at various spatial and temporal scales creates unprecedented opportunities to understand the complex and dynamic systems (e.g., plant invasion). Time series remote sensing is becoming more and more important to monitor the earth system dynamics and interactions. To date, most of the time series remote sensing studies have been conducted with the images acquired at coarse spatial scale, due to their relatively high temporal resolution. The construction of time series at fine spatial scale, however, is limited to few or discrete images acquired within or across years. The objective of this research is to advance the time series remote sensing at fine spatial scale, particularly to shift from discrete time series remote sensing to continuous time series remote sensing. The objective will be achieved through the following aims: 1) Advance intra-annual time series remote sensing under the pure-pixel assumption; 2) Advance intra-annual time series remote sensing under the mixed-pixel assumption; 3) Advance inter-annual time series remote sensing in monitoring the land surface dynamics; and 4) Advance the species distribution model with time series remote sensing. Taking invasive saltcedar as an example, four methods (i.e., phenological time series remote sensing model, temporal partial unmixing method, multiyear spectral angle clustering model, and time series remote sensing-based spatially explicit species distribution model) were developed to achieve the objectives. Results indicated that the phenological time series remote sensing model could effectively map saltcedar distributions through characterizing the seasonal phenological dynamics of plant species throughout the year. The proposed temporal partial unmixing method, compared to conventional unmixing methods, could more accurately estimate saltcedar abundance within a pixel by exploiting the adequate temporal signatures of saltcedar. The multiyear spectral angle clustering model could guide the selection of the most representative remotely sensed image for repetitive saltcedar mapping over space and time. Through incorporating spatial autocorrelation, the species distribution model developed in the study could identify the suitable habitats of saltcedar at a fine spatial scale and locate appropriate areas at high risk of saltcedar infestation. Among 10 environmental variables, the distance to the river and the phenological attributes summarized by the time series remote sensing were regarded as the most important. These methods developed in the study provide new perspectives on how the continuous time series can be leveraged under various conditions to investigate the plant invasion dynamics.

Forest mensuration with remote sensing: A retrospective and a vision for the future

Treesearch

Randolph H. Wynne

2004-01-01

Remote sensing, while occasionally oversold, has clear potential to reduce the overall cost of traditional forest inventories. Perhaps most important, some of the information needed for more intensive, rather than extensive, forest management is available from remote sensing. These new information needs may justify increased use and the increased cost of remote sensing...

15 CFR 960.12 - Data policy for remote sensing space systems.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 15 Commerce and Foreign Trade 3 2010-01-01 2010-01-01 false Data policy for remote sensing space... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.12 Data policy for remote sensing space systems. (a) In accordance with the Act, if the U.S. Government...

Remote Sensing: Analyzing Satellite Images to Create Higher Order Thinking Skills.

ERIC Educational Resources Information Center

Marks, Steven K.; And Others

1996-01-01

Presents a unit that uses remote-sensing images from satellites and other spacecraft to provide new perspectives of the earth and generate greater global awareness. Relates the levels of Bloom's hierarchy to different aspects of the remote sensing unit to confirm that the concepts and principles of remote sensing and related images belong in…

15 CFR 960.12 - Data policy for remote sensing space systems.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 15 Commerce and Foreign Trade 3 2011-01-01 2011-01-01 false Data policy for remote sensing space... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.12 Data policy for remote sensing space systems. (a) In accordance with the Act, if the U.S. Government...

15 CFR 960.12 - Data policy for remote sensing space systems.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 15 Commerce and Foreign Trade 3 2012-01-01 2012-01-01 false Data policy for remote sensing space... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.12 Data policy for remote sensing space systems. (a) In accordance with the Act, if the U.S. Government...

15 CFR 960.12 - Data policy for remote sensing space systems.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 15 Commerce and Foreign Trade 3 2014-01-01 2014-01-01 false Data policy for remote sensing space... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.12 Data policy for remote sensing space systems. (a) In accordance with the Act, if the U.S. Government...

15 CFR 960.12 - Data policy for remote sensing space systems.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 15 Commerce and Foreign Trade 3 2013-01-01 2013-01-01 false Data policy for remote sensing space... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.12 Data policy for remote sensing space systems. (a) In accordance with the Act, if the U.S. Government...

Annotated bibliography of remote sensing methods for monitoring desertification

USGS Publications Warehouse

Walker, A.S.; Robinove, Charles J.

1981-01-01

Remote sensing techniques are valuable for locating, assessing, and monitoring desertification. Remotely sensed data provide a permanent record of the condition of the land in a format that allows changes in land features and condition to be measured. The annotated bibliography of 118 items discusses remote sensing methods that may be applied to desertification studies.

Applied Remote Sensing Program (ARSP)

NASA Technical Reports Server (NTRS)

Johnson, J. D.; Foster, K. E.; Mouat, D. A.; Miller, D. A.; Conn, J. S.

1976-01-01

The activities and accomplishments of the Applied Remote Sensing Program during FY 1975-1976 are reported. The principal objective of the Applied Remote Sensing Program continues to be designed projects having specific decision-making impacts as a principal goal. These projects are carried out in cooperation and collaboration with local, state and federal agencies whose responsibilities lie with planning, zoning and environmental monitoring and/or assessment in the application of remote sensing techniques. The end result of the projects is the use by the involved agencies of remote sensing techniques in problem solving.

Communicating remote sensing concepts in an interdisciplinary environment

NASA Technical Reports Server (NTRS)

Chung, R.

1981-01-01

Although remote sensing is currently multidisciplinary in its applications, many of its terms come from the engineering sciences, particularly from the field of pattern recognition. Scholars from fields such as the social sciences, botany, and biology, may experience initial difficulty with remote sensing terminology, even though parallel concepts exist in their own fields. Some parallel concepts and terminologies from nonengineering fields, which might enhance the understanding of remote sensing concepts in an interdisciplinary situation are identified. Feedbacks which this analogue strategy might have on remote sensing itself are explored.

People, Places and Pixels: Remote Sensing in the Service of Society

NASA Technical Reports Server (NTRS)

Lulla, Kamlesh

2003-01-01

What is the role of Earth remote sensing and other geospatial technologies in our society? Recent global events have brought into focus the role of geospatial science and technology such as remote sensing, GIS, GPS in assisting the professionals who are responsible for operations such as rescue and recovery of sites after a disaster or a terrorist act. This paper reviews the use of recent remote sensing products from satellites such as IKONOS in these efforts. Aerial and satellite imagery used in land mine detection has been evaluated and the results of this evaluation will be discussed. Synopsis of current and future ISS Earth Remote Sensing capabilities will be provided. The role of future missions in humanitarian use of remote sensing will be explored.

NASA's High Mountain Asia Team (HiMAT): collaborative research to study changes of the High Asia region

NASA Astrophysics Data System (ADS)

Arendt, A. A.; Houser, P.; Kapnick, S. B.; Kargel, J. S.; Kirschbaum, D.; Kumar, S.; Margulis, S. A.; McDonald, K. C.; Osmanoglu, B.; Painter, T. H.; Raup, B. H.; Rupper, S.; Tsay, S. C.; Velicogna, I.

2017-12-01

The High Mountain Asia Team (HiMAT) is an assembly of 13 research groups funded by NASA to improve understanding of cryospheric and hydrological changes in High Mountain Asia (HMA). Our project goals are to quantify historical and future variability in weather and climate over the HMA, partition the components of the water budget across HMA watersheds, explore physical processes driving changes, and predict couplings and feedbacks between physical and human systems through assessment of hazards and downstream impacts. These objectives are being addressed through analysis of remote sensing datasets combined with modeling and assimilation methods to enable data integration across multiple spatial and temporal scales. Our work to date has focused on developing improved high resolution precipitation, snow cover and snow water equivalence products through a variety of statistical uncertainty analysis, dynamical downscaling and assimilation techniques. These and other high resolution climate products are being used as input and validation for an assembly of land surface and General Circulation Models. To quantify glacier change in the region we have calculated multidecadal mass balances of a subset of HMA glaciers by comparing commercial satellite imagery with earlier elevation datasets. HiMAT is using these tools and datasets to explore the impact of atmospheric aerosols and surface impurities on surface energy exchanges, to determine drivers of glacier and snowpack melt rates, and to improve our capacity to predict future hydrological variability. Outputs from the climate and land surface assessments are being combined with landslide and glacier lake inventories to refine our ability to predict hazards in the region. Economic valuation models are also being used to assess impacts on water resources and hydropower. Field data of atmospheric aerosol, radiative flux and glacier lake conditions are being collected to provide ground validation for models and remote sensing products. In this presentation we will discuss initial results and outline plans for a scheduled release of our datasets and findings to the broader community. We will also describe our methods for cross-team collaboration through the adoption of cloud computing and data integration tools.

Addressing critical environmental data gaps via low-cost, real-time, cellular-based environmental monitoring

NASA Astrophysics Data System (ADS)

Caylor, K. K.; Wolf, A.; Siegfried, B.

2014-12-01

Models in the environmental sciences are repositories in a sense of the current state of understanding of critical processes. However, as our understanding of these processes (and their accompanying models) become more granular, the data requirements to parameterize them become more limiting. In addition, as these models become more useful, they are often pressed into service for decision support, meaning that they cannot accept the data latency typical of most environmental observations. Finally, the vast majority of environmental data is generated at highly-instrumented, infrastructure-rich "mega sites" in the US/Europe, while many of the most pressing environmental issues are in rural locales and in the developing world. Cellular-based environmental sensing is a promising means to provide granular data in real time from remote locales to improve model-based forecasting using data assimilation. Applications we are working on include drought forecasting and food security; forest and crop responses to weather and climate change; and rural water usage. Over the past two years, we have developed a suite of integrated hardware, firmware, and backend APIs that accommodates an unlimited variety of sensors, and propagates these data onto the internet over mobile networks. Scientific data holds a unique role for demanding well-characterized information on sensor error and our design attempts to balance error reduction with low costs. The result is a deployment system that undercuts competing commercial products by as much as 90%, allowing more ubiquitous deployment with lower risks associated with sensor loss. Enclosure design and power management are critical ingredients for remote deployments under variable environmental conditions. Sensors push data onto cloud storage and make this data available via public API's via a backend server that accommodates additional metadata essential for interpreting observations, particularly their measurement errors. The data these pods collect can expand weather monitoring, but more crucially can monitor otherwise unobserved biological (including human) responses to environmental drivers. These data in turn can be assimilated into models, as a means to contextualize and distill these noisy observations into actionable knowledge.

Multi-model perspectives and inter-comparison of soil moisture and evapotranspiration in East Africa—an application of Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS)

NASA Astrophysics Data System (ADS)

Pervez, M. S.; McNally, A.; Arsenault, K. R.

2017-12-01

Convergence of evidence from different agro-hydrologic sources is particularly important for drought monitoring in data sparse regions. In Africa, a combination of remote sensing and land surface modeling experiments are used to evaluate past, present and future drought conditions. The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) routinely simulates daily soil moisture, evapotranspiration (ET) and other variables over Africa using multiple models and inputs. We found that Noah 3.3, Variable Infiltration Capacity (VIC) 4.1.2, and Catchment Land Surface Model based FLDAS simulations of monthly soil moisture percentile maps captured concurrent drought and water surplus episodes effectively over East Africa. However, the results are sensitive to selection of land surface model and hydrometeorological forcings. We seek to identify sources of uncertainty (input, model, parameter) to eventually improve the accuracy of FLDAS outputs. In absence of in situ data, previous work used European Space Agency Climate Change Initiative Soil Moisture (CCI-SM) data measured from merged active-passive microwave remote sensing to evaluate FLDAS soil moisture, and found that during the high rainfall months of April-May and November-December Noah-based soil moisture correlate well with CCI-SM over the Greater Horn of Africa region. We have found good correlations (r>0.6) for FLDAS Noah 3.3 ET anomalies and Operational Simplified Surface Energy Balance (SSEBop) ET over East Africa. Recently, SSEBop ET estimates (version 4) were improved by implementing a land surface temperature correction factor. We re-evaluate the correlations between FLDAS ET and version 4 SSEBop ET. To further investigate the reasons for differences between models we evaluate FLDAS soil moisture with Advanced Scatterometer and SMAP soil moisture and FLDAS outputs with MODIS and AVHRR normalized difference vegetation index. By exploring longer historic time series and near-real time products we will be aiding convergence of evidence for better understanding of historic drought, improved monitoring and forecasting, and better understanding of uncertainties of water availability estimation over Africa

Space-Time Controls on Carbon Sequestration Over Large-Scale Amazon Basin

NASA Technical Reports Server (NTRS)

Smith, Eric A.; Cooper, Harry J.; Gu, Jiujing; Grose, Andrew; Norman, John; daRocha, Humberto R.; Starr, David O. (Technical Monitor)

2002-01-01

A major research focus of the LBA Ecology Program is an assessment of the carbon budget and the carbon sequestering capacity of the large scale forest-pasture system that dominates the Amazonia landscape, and its time-space heterogeneity manifest in carbon fluxes across the large scale Amazon basin ecosystem. Quantification of these processes requires a combination of in situ measurements, remotely sensed measurements from space, and a realistically forced hydrometeorological model coupled to a carbon assimilation model, capable of simulating details within the surface energy and water budgets along with the principle modes of photosynthesis and respiration. Here we describe the results of an investigation concerning the space-time controls of carbon sources and sinks distributed over the large scale Amazon basin. The results are derived from a carbon-water-energy budget retrieval system for the large scale Amazon basin, which uses a coupled carbon assimilation-hydrometeorological model as an integrating system, forced by both in situ meteorological measurements and remotely sensed radiation fluxes and precipitation retrieval retrieved from a combination of GOES, SSM/I, TOMS, and TRMM satellite measurements. Brief discussion concerning validation of (a) retrieved surface radiation fluxes and precipitation based on 30-min averaged surface measurements taken at Ji-Parana in Rondonia and Manaus in Amazonas, and (b) modeled carbon fluxes based on tower CO2 flux measurements taken at Reserva Jaru, Manaus and Fazenda Nossa Senhora. The space-time controls on carbon sequestration are partitioned into sets of factors classified by: (1) above canopy meteorology, (2) incoming surface radiation, (3) precipitation interception, and (4) indigenous stomatal processes varied over the different land covers of pristine rainforest, partially, and fully logged rainforests, and pasture lands. These are the principle meteorological, thermodynamical, hydrological, and biophysical control paths which perturb net carbon fluxes and sequestration, produce time-space switching of carbon sources and sinks, undergo modulation through atmospheric boundary layer feedbacks, and respond to any discontinuous intervention on the landscape itself such as produced by human intervention in converting rainforest to pasture or conducting selective/clearcut logging operations.

Evaluation of the Event Driven Phenology Model Coupled with the VegET Evapotranspiration Model Through Comparisons with Reference Datasets in a Spatially Explicit Manner

NASA Technical Reports Server (NTRS)

Kovalskyy, V.; Henebry, G. M.; Adusei, B.; Hansen, M.; Roy, D. P.; Senay, G.; Mocko, D. M.

2011-01-01

A new model coupling scheme with remote sensing data assimilation was developed for estimation of daily actual evapotranspiration (ET). The scheme represents a mix of the VegET, a physically based model to estimate ET from a water balance, and an event driven phenology model (EDPM), where the EDPM is an empirically derived crop specific model capable of producing seasonal trajectories of canopy attributes. In this experiment, the scheme was deployed in a spatially explicit manner within the croplands of the Northern Great Plains. The evaluation was carried out using 2007-2009 land surface forcing data from the North American Land Data Assimilation System (NLDAS) and crop maps derived from remotely sensed data of NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). We compared the canopy parameters produced by the phenology model with normalized difference vegetation index (NDVI) data derived from the MODIS nadir bi-directional reflectance distribution function (BRDF) adjusted reflectance (NBAR) product. The expectations of the EDPM performance in prognostic mode were met, producing determination coefficient (r2) of 0.8 +/-.0.15. Model estimates of NDVI yielded root mean square error (RMSE) of 0.1 +/-.0.035 for the entire study area. Retrospective correction of canopy dynamics with MODIS NDVI brought the errors down to just below 10% of observed data range. The ET estimates produced by the coupled scheme were compared with ones from the MODIS land product suite. The expected r2=0.7 +/-.15 and RMSE = 11.2 +/-.4 mm per 8 days were met and even exceeded by the coupling scheme0 functioning in both prognostic and retrospective modes. Minor setbacks of the EDPM and VegET performance (r2 about 0.5 and additional 30 % of RMSR) were found on the peripheries of the study area and attributed to the insufficient EDPM training and to spatially varying accuracy of crop maps. Overall the experiment provided sufficient evidence of soundness and robustness of the EDPM and VegET coupling scheme, assuring its potential for spatially explicit applications.

Advancing Littoral Zone Aerosol Prediction via Holistic Studies in Regime-Dependent Flows: August 3-9, 2016 Middle East Dust Event

NASA Astrophysics Data System (ADS)

Solbrig, J. E.; Miller, S. D.; van den Heever, S. C.; Kreidenweis, S. M.; Oo, M. M.; Zupanski, M.; Zhang, J.; Wang, J.; Holz, R.; Albers, S. C.; Grasso, L. D.; Kliewer, A.; Bukowski, J.; Park, J.; Saleeby, S. M.; Wu, T. C.

2017-12-01

Coastal regions represent a complex environment for meteorological processes, their effect on aerosol distributions, and the resulting impacts of those aerosols. These regions are rife with discontinuities that make dynamical processes difficult to predict and confound optical retrieval algorithms with highly variable and poorly characterized backgrounds. Local dynamics can be complicated by interactions between maritime and continental airmasses and the presence of coastal terrain. Additionally, coastal shallow water and high-turbidity produce backgrounds with high water leaving radiance which biases results from remote sensing retrievals. Here we present the highlights of the first two years of work from a Multi-disciplinary University Research Initiative entitled Holistic Analysis of Aerosol in Littoral Environments (HAALE-MURI) with specific focus on a dust event that occurred during the period of August 3-9 2016. During this period, two large dust plumes were observed advecting across the Arabian Peninsula. The first, embedded in a dry airmass, moved across the peninsula from north-west to south-east. This plume eventually stalls as it encounters a moist airmass, likely driven by the sea breeze. Embedded in the moist airmass is a second dust plume lofted from Oman, which then advects northwards over the Persian Gulf. This case presents significant challenges for forecasting, remote sensing, and data assimilation due to a complex meteorological environment and variable coastal bright-water backgrounds. The project team, who endeavor to advance our fundamental understanding of the factors that govern aerosol distribution, optical properties, and microphysical properties in the coastal regions, have focused on this case as our first in-depth case study. We demonstrate new retrieval techniques during both day and night including retrievals over bright coastal waters, a novel approach to in-line data assimilation of aerosol properties including AOT, and the results of model sensitivity studies. We take a holistic approach to better understanding aerosol processes in a coupled system as opposed to stand-alone analyses that fail to account for the myriad of covariances that exist in any real-world scenario.

The application of remote sensing techniques to the study of ophiolites

NASA Astrophysics Data System (ADS)

Khan, Shuhab D.; Mahmood, Khalid

2008-08-01

Satellite remote sensing methods are a powerful tool for detailed geologic analysis, especially in inaccessible regions of the earth's surface. Short-wave infrared (SWIR) bands are shown to provide spectral information bearing on the lithologic, structural, and geochemical character of rock bodies such as ophiolites, allowing for a more comprehensive assessment of the lithologies present, their stratigraphic relationships, and geochemical character. Most remote sensing data are widely available for little or no cost, along with user-friendly software for non-specialists. In this paper we review common remote sensing systems and methods that allow for the discrimination of solid rock (lithologic) components of ophiolite complexes and their structural relationships. Ophiolites are enigmatic rock bodies which associated with most, if not all, plate collision sutures. Ophiolites are ideal for remote sensing given their widely recognized diversity of lithologic types and structural relationships. Accordingly, as a basis for demonstrating the utility of remote sensing techniques, we briefly review typical ophiolites in the Tethyan tectonic belt. As a case study, we apply integrated remote sensing studies of a well-studied example, the Muslim Bagh ophiolite, located in Balochistan, western Pakistan. On this basis, we attempt to demonstrate how remote sensing data can validate and reconcile existing information obtained from field studies. The lithologic and geochemical diversity of Muslim Bagh are representative of Tethyan ophiolites. Despite it's remote location it has been extensively mapped and characterized by structural and geochemical studies, and is virtually free of vegetative cover. Moreover, integrating the remote sensing data with 'ground truth' information thus offers the potential of an improved template for interpreting remote sensing data sets of other ophiolites for which little or no field information is available.

Remote Sensing and Remote Control Activities in Europe and America: Part 2--Remote Sensing Ground Stations in Europe,

DTIC Science & Technology

1996-04-08

Development tasks and products of remote sensing ground stations in Europe are represented by the In-Sec Corporation and the Schlumberger Industries Corporation. The article presents the main products of these two corporations.

An Earth Observation Land Data Assimilation System for Data from Multiple Wavelength Domains: Water and Energy Balance Components

NASA Astrophysics Data System (ADS)

Quaife, T. L.; Davenport, I. J.; Lines, E.; Styles, J.; Lewis, P.; Gurney, R. J.

2012-12-01

Satellite observations offer a spatially and temporally synoptic data source for constraining models of land surface processes, but exploitation of these data for such purposes has been largely ad-hoc to date. In part this is because traditional land surface models, and hence most land surface data assimilation schemes, have tended to focus on a specific component of the land surface problem; typically either surface fluxes of water and energy or biogeochemical cycles such as carbon and nitrogen. Furthermore the assimilation of satellite data into such models tends to be restricted to a single wavelength domain, for example passive microwave, thermal or optical, depending on the problem at hand. The next generation of land surface schemes, such as the Joint UK Land Environment Simulator (JULES) and the US Community Land Model (CLM) represent a broader range of processes but at the expense of increasing overall model complexity and in some cases reducing the level of detail in specific processes to accommodate this. Typically, the level of physical detail used to represent the interaction of electromagnetic radiation with the surface is not sufficient to enable prediction of intrinsic satellite observations (reflectance, brightness temperature and so on) and consequently these are not assimilated directly into the models. A seemingly attractive alternative is to assimilate high-level products derived from satellite observations but these are often only superficially related to the corresponding variables in land surface models due to conflicting assumptions between the two. This poster describes the water and energy balance modeling components of a project funded by the European Space Agency to develop a data assimilation scheme for the land surface and observation operators to translate between models and the intrinsic observations acquired by satellite missions. The rationale behind the design of the underlying process model is to represent the physics of the water and energy balance in as parsimonious manner as possible, using a force-restore approach, but describing the physics of electromagnetic radiation scattering at the surface sufficiently well that it is possible to assimilate the intrinsic observations made by remote sensing instruments. In this manner the initial configuration of the resulting scheme will be able to make optimal use of available satellite observations at arbitrary wavelengths and geometries. Model complexity can then be built up from this point whilst ensuring consistency with satellite observations.

Open source data assimilation framework for hydrological modeling

NASA Astrophysics Data System (ADS)

Ridler, Marc; Hummel, Stef; van Velzen, Nils; Katrine Falk, Anne; Madsen, Henrik

2013-04-01

An open-source data assimilation framework is proposed for hydrological modeling. Data assimilation (DA) in hydrodynamic and hydrological forecasting systems has great potential to improve predictions and improve model result. The basic principle is to incorporate measurement information into a model with the aim to improve model results by error minimization. Great strides have been made to assimilate traditional in-situ measurements such as discharge, soil moisture, hydraulic head and snowpack into hydrologic models. More recently, remotely sensed data retrievals of soil moisture, snow water equivalent or snow cover area, surface water elevation, terrestrial water storage and land surface temperature have been successfully assimilated in hydrological models. The assimilation algorithms have become increasingly sophisticated to manage measurement and model bias, non-linear systems, data sparsity (time & space) and undetermined system uncertainty. It is therefore useful to use a pre-existing DA toolbox such as OpenDA. OpenDA is an open interface standard for (and free implementation of) a set of tools to quickly implement DA and calibration for arbitrary numerical models. The basic design philosophy of OpenDA is to breakdown DA into a set of building blocks programmed in object oriented languages. To implement DA, a model must interact with OpenDA to create model instances, propagate the model, get/set variables (or parameters) and free the model once DA is completed. An open-source interface for hydrological models exists capable of all these tasks: OpenMI. OpenMI is an open source standard interface already adopted by key hydrological model providers. It defines a universal approach to interact with hydrological models during simulation to exchange data during runtime, thus facilitating the interactions between models and data sources. The interface is flexible enough so that models can interact even if the model is coded in a different language, represent processes from a different domain or have different spatial and temporal resolutions. An open source framework that bridges OpenMI and OpenDA is presented. The framework provides a generic and easy means for any OpenMI compliant model to assimilate observation measurements. An example test case will be presented using MikeSHE, and OpenMI compliant fully coupled integrated hydrological model that can accurately simulate the feedback dynamics of overland flow, unsaturated zone and saturated zone.

[Estimation of desert vegetation coverage based on multi-source remote sensing data].

PubMed

Wan, Hong-Mei; Li, Xia; Dong, Dao-Rui

2012-12-01

Taking the lower reaches of Tarim River in Xinjiang of Northwest China as study areaAbstract: Taking the lower reaches of Tarim River in Xinjiang of Northwest China as study area and based on the ground investigation and the multi-source remote sensing data of different resolutions, the estimation models for desert vegetation coverage were built, with the precisions of different estimation methods and models compared. The results showed that with the increasing spatial resolution of remote sensing data, the precisions of the estimation models increased. The estimation precision of the models based on the high, middle-high, and middle-low resolution remote sensing data was 89.5%, 87.0%, and 84.56%, respectively, and the precisions of the remote sensing models were higher than that of vegetation index method. This study revealed the change patterns of the estimation precision of desert vegetation coverage based on different spatial resolution remote sensing data, and realized the quantitative conversion of the parameters and scales among the high, middle, and low spatial resolution remote sensing data of desert vegetation coverage, which would provide direct evidence for establishing and implementing comprehensive remote sensing monitoring scheme for the ecological restoration in the study area.

Grid workflow validation using ontology-based tacit knowledge: A case study for quantitative remote sensing applications

NASA Astrophysics Data System (ADS)

Liu, Jia; Liu, Longli; Xue, Yong; Dong, Jing; Hu, Yingcui; Hill, Richard; Guang, Jie; Li, Chi

2017-01-01

Workflow for remote sensing quantitative retrieval is the ;bridge; between Grid services and Grid-enabled application of remote sensing quantitative retrieval. Workflow averts low-level implementation details of the Grid and hence enables users to focus on higher levels of application. The workflow for remote sensing quantitative retrieval plays an important role in remote sensing Grid and Cloud computing services, which can support the modelling, construction and implementation of large-scale complicated applications of remote sensing science. The validation of workflow is important in order to support the large-scale sophisticated scientific computation processes with enhanced performance and to minimize potential waste of time and resources. To research the semantic correctness of user-defined workflows, in this paper, we propose a workflow validation method based on tacit knowledge research in the remote sensing domain. We first discuss the remote sensing model and metadata. Through detailed analysis, we then discuss the method of extracting the domain tacit knowledge and expressing the knowledge with ontology. Additionally, we construct the domain ontology with Protégé. Through our experimental study, we verify the validity of this method in two ways, namely data source consistency error validation and parameters matching error validation.

Impact of CAMEX-4 Data Sets for Hurricane Forecasts using a Global Model

NASA Technical Reports Server (NTRS)

Kamineni, Rupa; Krishnamurti, T. N.; Pattnaik, S.; Browell, Edward V.; Ismail, Syed; Ferrare, Richard A.

2005-01-01

This study explores the impact on hurricane data assimilation and forecasts from the use of dropsondes and remote-sensed moisture profiles from the airborne Lidar Atmospheric Sensing Experiment (LASE) system. We show that the use of these additional data sets, above those from the conventional world weather watch, has a positive impact on hurricane predictions. The forecast tracks and intensity from the experiments show a marked improvement compared to the control experiment where such data sets were excluded. A study of the moisture budget in these hurricanes showed enhanced evaporation and precipitation over the storm area. This resulted in these data sets making a large impact on the estimate of mass convergence and moisture fluxes, which were much smaller in the control runs. Overall this study points to the importance of high vertical resolution humidity data sets for improved model results. We note that the forecast impact from the moisture profiling data sets for some of the storms is even larger than the impact from the use of dropwindsonde based winds.

An Approach of Registration between Remote Sensing Image and Electronic Chart Based on Coastal Line

NASA Astrophysics Data System (ADS)

Li, Ying; Yu, Shuiming; Li, Chuanlong

Remote sensing plays an important role marine oil spill emergency. In order to implement a timely and effective countermeasure, it is important to provide exact position of oil spills. Therefore it is necessary to match remote sensing image and electronic chart properly. Variance ordinarily exists between oil spill image and electronic chart, although geometric correction is applied to remote sensing image. It is difficult to find the steady control points on sea to make exact rectification of remote sensing image. An improved relaxation algorithm was developed for finding the control points along the coastline since oil spills occurs generally near the coast. A conversion function is created with the least square, and remote sensing image can be registered with the vector map based on this function. SAR image was used as the remote sensing data and shape format map as the electronic chart data. The results show that this approach can guarantee the precision of the registration, which is essential for oil spill monitoring.

The U.S. Geological Survey land remote sensing program

USGS Publications Warehouse

Saunders, T.; Feuquay, J.; Kelmelis, J.A.

2003-01-01

The U.S. Geological Survey has been a provider of remotely sensed information for decades. As the availability and use of satellite data has grown, USGS has placed increasing emphasis on expanding the knowledge about the science of remote sensing and on making remotely sensed data more accessible. USGS encourages widespread availability and distribution of these data and through its programs, encourages and enables a variety of research activities and the development of useful applications of the data. The science of remote sensing has great potential for assisting in the monitoring and assessment of the impacts of natural disasters, management and analysis of environmental, biological, energy, and mineral investigations, and supporting informed public policy decisions. By establishing the Land Remote Sensing Program (LRS) as a major unit of the USGS Geography Program, USGS has taken the next step to further increase support for the accessibility, understanding, and use of remotely sensed data. This article describes the LRS Program, its mission and objectives, and how the program has been structured to accomplish its goals.

Microwave Remote Sensing Modeling of Ocean Surface Salinity and Winds Using an Empirical Sea Surface Spectrum

NASA Technical Reports Server (NTRS)

Yueh, Simon H.

2004-01-01

Active and passive microwave remote sensing techniques have been investigated for the remote sensing of ocean surface wind and salinity. We revised an ocean surface spectrum using the CMOD-5 geophysical model function (GMF) for the European Remote Sensing (ERS) C-band scatterometer and the Ku-band GMF for the NASA SeaWinds scatterometer. The predictions of microwave brightness temperatures from this model agree well with satellite, aircraft and tower-based microwave radiometer data. This suggests that the impact of surface roughness on microwave brightness temperatures and radar scattering coefficients of sea surfaces can be consistently characterized by a roughness spectrum, providing physical basis for using combined active and passive remote sensing techniques for ocean surface wind and salinity remote sensing.

Online catalog access and distribution of remotely sensed information

NASA Astrophysics Data System (ADS)

Lutton, Stephen M.

1997-09-01

Remote sensing is providing voluminous data and value added information products. Electronic sensors, communication electronics, computer software, hardware, and network communications technology have matured to the point where a distributed infrastructure for remotely sensed information is a reality. The amount of remotely sensed data and information is making distributed infrastructure almost a necessity. This infrastructure provides data collection, archiving, cataloging, browsing, processing, and viewing for applications from scientific research to economic, legal, and national security decision making. The remote sensing field is entering a new exciting stage of commercial growth and expansion into the mainstream of government and business decision making. This paper overviews this new distributed infrastructure and then focuses on describing a software system for on-line catalog access and distribution of remotely sensed information.

Remote Sensing and the Environment.

ERIC Educational Resources Information Center

Osmers, Karl

1991-01-01

Suggests using remote sensing technology to help students make sense of the natural world. Explains that satellite information allows observation of environmental changes over time. Identifies possible student projects based on remotely sensed data. Recommends obtaining the assistance of experts and seeking funding through effective project…

Use of remote sensing in agriculture

NASA Technical Reports Server (NTRS)

Pettry, D. E.; Powell, N. L.; Newhouse, M. E.

1974-01-01

Remote sensing studies in Virginia and Chesapeake Bay areas to investigate soil and plant conditions via remote sensing technology are reported ant the results given. Remote sensing techniques and interactions are also discussed. Specific studies on the effects of soil moisture and organic matter on energy reflection of extensively occurring Sassafras soils are discussed. Greenhouse and field studies investigating the effects of chlorophyll content of Irish potatoes on infrared reflection are presented. Selected ground truth and environmental monitoring data are shown in summary form. Practical demonstrations of remote sensing technology in agriculture are depicted and future use areas are delineated.

Applications of remote sensing to watershed management

NASA Technical Reports Server (NTRS)

Rango, A.

1975-01-01

Aircraft and satellite remote sensing systems which are capable of contributing to watershed management are described and include: the multispectral scanner subsystem on LANDSAT and the basic multispectral camera array flown on high altitude aircraft such as the U-2. Various aspects of watershed management investigated by remote sensing systems are discussed. Major areas included are: snow mapping, surface water inventories, flood management, hydrologic land use monitoring, and watershed modeling. It is indicated that technological advances in remote sensing of hydrological data must be coupled with an expansion of awareness and training in remote sensing techniques of the watershed management community.

NASA Glenn OHIOVIEW FY01/02 Project

NASA Technical Reports Server (NTRS)

2003-01-01

The results of the research performed by the university principal investigators are herein compiled. OhioView's general goals were: 1) To increase remote sensing education for Ohio s undergraduate and graduate students, and also enhancing curriculum in the mathematics and science for K-12 students using the capabilities of remote sensing; 2) To conduct advanced research to develop novel remote sensing applications, i.e. to turn data into information for more applications; 3) To maximize the use of remote sensing technology by the general public through outreach and the development of tools for more user-friendly access to remote sensing data.

The availability of conventional forms of remotely sensed data

USGS Publications Warehouse

Sturdevant, James A.; Holm, Thomas M.

1982-01-01

For decades Federal and State agencies have been collecting aerial photographs of various film types and scales over parts of the United States. More recently, worldwide Earth resources data acquired by orbiting satellites have inundated the remote sensing community. Determining the types of remotely sensed data that are publicly available can be confusing to the land-resource manager, planner, and scientist. This paper is a summary of the more commonly used types of remotely sensed data (aircraft and satellite) and their public availability. Special emphasis is placed on the National High-Altitude Photography (NHAP) program and future remote-sensing satellites.

NASA's Applied Remote Sensing Training (ARSET) Webinar Series

Atmospheric Science Data Center

2016-07-12

NASA's Applied Remote Sensing Training (ARSET) Webinar Series Tuesday, July 12, 2016 ... you of a free training opportunity: Introduction to Remote Sensing for Air Quality Applications Webinar Series Beginning in ...

Tropospheric Passive Remote Sensing

NASA Technical Reports Server (NTRS)

Keafer, L. S., Jr. (Editor)

1982-01-01

The long term role of airborne/spaceborne passive remote sensing systems for tropospheric air quality research and the identification of technology advances required to improve the performance of passive remote sensing systems were discussed.

Remote Sensing as a Demonstration of Applied Physics.

ERIC Educational Resources Information Center

Colwell, Robert N.

1980-01-01

Provides information about the field of remote sensing, including discussions of geo-synchronous and sun-synchronous remote-sensing platforms, the actual physical processes and equipment involved in sensing, the analysis of images by humans and machines, and inexpensive, small scale methods, including aerial photography. (CS)

Opportunities and problems in introducing or expanding the teaching of remote sensing in universities

NASA Technical Reports Server (NTRS)

Maxwell, E. L.

1980-01-01

The need for degree programs in remote sensing is considered. Any education program which claims to train remote sensing specialists must include expertise in the physical principles upon which remote sensing is based. These principles dictate the limits of engineering and design, computer analysis, photogrammetry, and photointerpretation. Faculty members must be hired to provide emphasis in those five areas.

Remote sensing of vegetation fires and its contribution to a fire management information system

Treesearch

Stephane P. Flasse; Simon N. Trigg; Pietro N. Ceccato; Anita H. Perryman; Andrew T. Hudak; Mark W. Thompson; Bruce H. Brockett; Moussa Drame; Tim Ntabeni; Philip E. Frost; Tobias Landmann; Johan L. le Roux

2004-01-01

In the last decade, research has proven that remote sensing can provide very useful support to fire managers. This chapter provides an overview of the types of information remote sensing can provide to the fire community. First, it considers fire management information needs in the context of a fire management information system. An introduction to remote sensing then...

Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing

DTIC Science & Technology

2016-07-15

AFRL-AFOSR-JP-TR-2016-0068 Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing Hean-Teik...SUBTITLE Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER... electromagnetics to the application in microwave remote sensing as well as extension of modelling capability with computational flexibility to study

Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing

DTIC Science & Technology

2016-07-15

AFRL-AFOSR-JP-TR-2016-0068 Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing Hean-Teik...SUBTITLE Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER...electromagnetics to the application in microwave remote sensing as well as extension of modelling capability with computational flexibility to study

Basic Remote Sensing Investigations for Beach Reconnaissance.

DTIC Science & Technology

Progress is reported on three tasks designed to develop remote sensing beach reconnaissance techniques applicable to the benthic, beach intertidal...and beach upland zones. Task 1 is designed to develop remote sensing indicators of important beach composition and physical parameters which will...ultimately prove useful in models to predict beach conditions. Task 2 is designed to develop remote sensing techniques for survey of bottom features in

Bridging the Scales from Field to Region with Practical Tools to Couple Time- and Space-Synchronized Data from Flux Towers and Networks with Proximal and Remote Sensing Data

NASA Astrophysics Data System (ADS)

Burba, G. G.; Avenson, T.; Burkart, A.; Gamon, J. A.; Guan, K.; Julitta, T.; Pastorello, G.; Sakowska, K.

2017-12-01

Many hundreds of flux towers are presently operational as standalone projects and as parts of regional networks. However, the vast majority of these towers do not allow straightforward coupling with remote sensing (drone, aircraft, satellite, etc.) data, and even fewer have optical sensors for validation of remote sensing products, and upscaling from field to regional levels. In 2016-2017, new tools to collect, process, and share time-synchronized flux data from multiple towers were developed and deployed globally. Originally designed to automate site and data management, and to streamline flux data analysis, these tools allow relatively easy matching of tower data with remote sensing data: GPS-driven PTP time protocol synchronizes instrumentation within the station, different stations with each other, and all of these to remote sensing data to precisely align remote sensing and flux data in time Footprint size and coordinates computed and stored with flux data help correctly align tower flux footprints and drone, aircraft or satellite motion to precisely align optical and flux data in space Full snapshot of the remote sensing pixel can then be constructed, including leaf-level, ground optical sensor, and flux tower measurements from the same footprint area, closely coupled with the remote sensing measurements to help interpret remote sensing data, validate models, and improve upscaling Additionally, current flux towers can be augmented with advanced ground optical sensors and can use standard routines to deliver continuous products (e.g. SIF, PRI, NDVI, etc.) based on automated field spectrometers (e.g., FloX and RoX, etc.) and other optical systems. Several dozens of new towers already operational globally can be readily used for the proposed workflow. Over 500 active traditional flux towers can be updated to synchronize their data with remote sensing measurements. This presentation will show how the new tools are used by major networks, and describe how this approach can be utilized for matching remote sensing and tower data to aid in ground truthing, improve scientific interactions, and promote joint grant writing and other forms of collaboration between the flux and remote sensing communities.

Monitoring Crop Phenology and Growth Stages from Space: Opportunities and Challenges

NASA Astrophysics Data System (ADS)

Gao, F.; Anderson, M. C.; Mladenova, I. E.; Kustas, W. P.; Alfieri, J. G.

2014-12-01

Crop growth stages in concert with weather and soil moisture conditions can have a significant impact on crop yields. In the U.S., crop growth stages and conditions are reported by farmers at the county level. These reports are somewhat subjective and fluctuate between different reporters, locations and times. Remote sensing data provide an alternative approach to monitoring crop growth over large areas in a more consistent and quantitative way. In the recent years, remote sensing data have been used to detect vegetation phenology at 1-km spatial resolution globally. However, agricultural applications at field scale require finer spatial resolution remote sensing data. Landsat (30-m) data have been successfully used for agricultural applications. There are many medium resolution sensors available today or in near future. These include Landsat, SPOT, RapidEye, ASTER and future Sentinel-2 etc. Approaches have been developed in the past several years to integrate remote sensing data from different sensors which may have different sensor characteristics, and spatial and temporal resolutions. This allows us opportunities today to map crop growth stages and conditions using dense time-series remote sensing at field scales. However, remotely sensed phenology (or phenological metrics) is normally derived based on the mathematical functions of the time-series data. The phenological metrics are determined by either identifying inflection (curvature) points or some pre-defined thresholds in the remote sensing phenology algorithms. Furthermore, physiological crop growth stages may not be directly correlated to the remotely sensed phenology. The relationship between remotely sensed phenology and crop growth stages is likely to vary for specific crop types and varieties, growing stages, conditions and even locations. In this presentation, we will examine the relationship between remotely sensed phenology and crop growth stages using in-situ measurements from Fluxnet sites and crop progress reports from USDA NASS. We will present remote sensing approaches and focus on: 1) integrating multiple sources of remote sensing data; and 2) extracting crop phenology at field scales. An example in the U.S. Corn Belt area will be presented and analyzed. Future directions for mapping crop growth stages will be discussed.

Remote Sensing: A Film Review.

ERIC Educational Resources Information Center

Carter, David J.

1986-01-01

Reviews the content of 19 films on remote sensing published between 1973 and 1980. Concludes that they are overly simplistic, notably outdated, and generally too optimistic about the potential of remote sensing from space for resource exploration and environmental problem-solving. Provides names and addresses of more current remote sensing…

Educational activities of remote sensing archaeology (Conference Presentation)

NASA Astrophysics Data System (ADS)

Hadjimitsis, Diofantos G.; Agapiou, Athos; Lysandrou, Vasilki; Themistocleous, Kyriacos; Cuca, Branka; Nisantzi, Argyro; Lasaponara, Rosa; Masini, Nicola; Krauss, Thomas; Cerra, Daniele; Gessner, Ursula; Schreier, Gunter

2016-10-01

Remote sensing science is increasingly being used to support archaeological and cultural heritage research in various ways. Satellite sensors either passive or active are currently used in a systematic basis to detect buried archaeological remains and to systematic monitor tangible heritage. In addition, airborne and low altitude systems are being used for documentation purposes. Ground surveys using remote sensing tools such as spectroradiometers and ground penetrating radars can detect variations of vegetation and soil respectively, which are linked to the presence of underground archaeological features. Education activities and training of remote sensing archaeology to young people is characterized of highly importance. Specific remote sensing tools relevant for archaeological research can be developed including web tools, small libraries, interactive learning games etc. These tools can be then combined and aligned with archaeology and cultural heritage. This can be achieved by presenting historical and pre-historical records, excavated sites or even artifacts under a "remote sensing" approach. Using such non-form educational approach, the students can be involved, ask, read, and seek to learn more about remote sensing and of course to learn about history. The paper aims to present a modern didactical concept and some examples of practical implementation of remote sensing archaeology in secondary schools in Cyprus. The idea was built upon an ongoing project (ATHENA) focused on the sue of remote sensing for archaeological research in Cyprus. Through H2020 ATHENA project, the Remote Sensing Science and Geo-Environment Research Laboratory at the Cyprus University of Technology (CUT), with the support of the National Research Council of Italy (CNR) and the German Aerospace Centre (DLR) aims to enhance its performance in all these new technologies.

SMAP Soil Moisture Disaggregation using Land Surface Temperature and Vegetation Data

NASA Astrophysics Data System (ADS)

Fang, B.; Lakshmi, V.

2016-12-01

Soil moisture (SM) is a key parameter in agriculture, hydrology and ecology studies. The global SM retrievals have been providing by microwave remote sensing technology since late 1970s and many SM retrieval algorithms have been developed, calibrated and applied on satellite sensors such as AMSR-E (Advanced Microwave Scanning Radiometer for the Earth Observing System), AMSR-2 (Advanced Microwave Scanning Radiometer 2) and SMOS (Soil Moisture and Ocean Salinity). Particularly, SMAP (Soil Moisture Active/Passive) satellite, which was developed by NASA, was launched in January 2015. SMAP provides soil moisture products of 9 km and 36 km spatial resolutions which are not capable for research and applications of finer scale. Toward this issue, this study applied a SM disaggregation algorithm to disaggregate SMAP passive microwave soil moisture 36 km product. This algorithm was developed based on the thermal inertial relationship between daily surface temperature variation and daily average soil moisture which is modulated by vegetation condition, by using remote sensing retrievals from AVHRR (Advanced Very High Resolution Radiometer, MODIS (Moderate Resolution Imaging Spectroradiometer), SPOT (Satellite Pour l'Observation de la Terre), as well as Land Surface Model (LSM) output from NLDAS (North American Land Data Assimilation System). The disaggregation model was built at 1/8o spatial resolution on monthly basis and was implemented to calculate and disaggregate SMAP 36 km SM retrievals to 1 km resolution in Oklahoma. The SM disaggregation results were also validated using MESONET (Mesoscale Network) and MICRONET (Microscale Network) ground SM measurements.

Simulation of Avifauna Distributions Using Remote Sensing

NASA Technical Reports Server (NTRS)

Smith, James A.

2004-01-01

Remote sensing has proved a fruitful tool for understanding the distribution and functioning of plant communities at multiple scales and to understand their coupling to bioclimatic and anthropogenic factors. But a similar approach to understanding the distribution and abundance of bird species as well as many other animal organisms is lacking. The increasing need for such understanding is evident with the recent examples of threats to human health via avian vector transmission and the increasing emphasis on global conservation biology. From experimental observations we know that species richness tends to track biological or environmental gradients. In this paper, we explore the fundamental idea that thermal and water-relation environments of birds, as estimated from satellite data and biophysical models, can define the constraints on their Occurrences and richness. We develop individual bird energy budget models and use these models to define the climate space niche of birds. Using satellite data assimilation products to drive our models, we disperse a distribution of virtual or actual bird species across the landscape in accordance to the limits expressed by their climate space niche. Here, we focus on the North American summer breeding season and give two examples to illustrate our approach. The first is a tundra loving bird, e.g. corresponding to the Culidris genus, and a second genus example, Myiurchus, that corresponds to arid or semi-arid regions. We define these birds in terms of their basic physiology and morphological characteristics, construct avian energetic simulations to predict their allowable metabolic ranges and climate space limits.

Crop monitoring & yield forecasting system based on Synthetic Aperture Radar (SAR) and process-based crop growth model: Development and validation in South and South East Asian Countries

NASA Astrophysics Data System (ADS)

Setiyono, T. D.

2014-12-01

Accurate and timely information on rice crop growth and yield helps governments and other stakeholders adapting their economic policies and enables relief organizations to better anticipate and coordinate relief efforts in the wake of a natural catastrophe. Such delivery of rice growth and yield information is made possible by regular earth observation using space-born Synthetic Aperture Radar (SAR) technology combined with crop modeling approach to estimate yield. Radar-based remote sensing is capable of observing rice vegetation growth irrespective of cloud coverage, an important feature given that in incidences of flooding the sky is often cloud-covered. The system allows rapid damage assessment over the area of interest. Rice yield monitoring is based on a crop growth simulation and SAR-derived key information, particularly start of season and leaf growth rate. Results from pilot study sites in South and South East Asian countries suggest that incorporation of SAR data into crop model improves yield estimation for actual yields. Remote-sensing data assimilation into crop model effectively capture responses of rice crops to environmental conditions over large spatial coverage, which otherwise is practically impossible to achieve. Such improvement of actual yield estimates offers practical application such as in a crop insurance program. Process-based crop simulation model is used in the system to ensure climate information is adequately captured and to enable mid-season yield forecast.

Remote Sensing and the Earth.

ERIC Educational Resources Information Center

Brosius, Craig A.; And Others

This document is designed to help senior high school students study remote sensing technology and techniques in relation to the environmental sciences. It discusses the acquisition, analysis, and use of ecological remote data. Material is divided into three sections and an appendix. Section One is an overview of the basics of remote sensing.…

Microwave remote sensing of snowpack properties

NASA Technical Reports Server (NTRS)

Rango, A. (Editor)

1980-01-01

Topic concerning remote sensing capabilities for providing reliable snow cover data and measurement of snow water equivalents are discussed. Specific remote sensing technqiues discussed include those in the microwave region of the electromagnetic spectrum.

Commerical Remote Sensing Data Contract

USGS Publications Warehouse

,

2005-01-01

The U. S. Geological Survey's (USGS) Commercial Remote Sensing Data Contracts (CRSDCs) provide government agencies with access to a broad range of commercially available remotely sensed airborne and satellite data. These contracts were established to support The National Map partners, other Federal Civilian agency programs, and Department of Defense programs that require data for the United States and its territories. Experience shows that centralized procurement of remotely sensed data leads to considerable cost savings to the Federal government through volume discounts, reduction of redundant contract administrative costs, and avoidance of duplicate purchases. These contracts directly support the President's Commercial Remote Sensing Space Policy, signed in 2003, by providing a centralized mechanism for civil agencies to acquire commercial remote sensing products to support their mission needs in an efficient and coordinated way. CRSDC administration is provided by the USGS Mid-Continent Mapping Center in Rolla, Missouri.

Object-oriented recognition of high-resolution remote sensing image

NASA Astrophysics Data System (ADS)

Wang, Yongyan; Li, Haitao; Chen, Hong; Xu, Yuannan

2016-01-01

With the development of remote sensing imaging technology and the improvement of multi-source image's resolution in satellite visible light, multi-spectral and hyper spectral , the high resolution remote sensing image has been widely used in various fields, for example military field, surveying and mapping, geophysical prospecting, environment and so forth. In remote sensing image, the segmentation of ground targets, feature extraction and the technology of automatic recognition are the hotspot and difficulty in the research of modern information technology. This paper also presents an object-oriented remote sensing image scene classification method. The method is consist of vehicles typical objects classification generation, nonparametric density estimation theory, mean shift segmentation theory, multi-scale corner detection algorithm, local shape matching algorithm based on template. Remote sensing vehicles image classification software system is designed and implemented to meet the requirements .

Irrigation Signals Detected From SMAP Soil Moisture Retrievals

NASA Astrophysics Data System (ADS)

Lawston, Patricia M.; Santanello, Joseph A.; Kumar, Sujay V.

2017-12-01

Irrigation can influence weather and climate, but the magnitude, timing, and spatial extent of irrigation are poorly represented in models, as are the resulting impacts of irrigation on the coupled land-atmosphere system. One way to improve irrigation representation in models is to assimilate soil moisture observations that reflect an irrigation signal to improve model states. Satellite remote sensing is a promising avenue for obtaining these needed observations on a routine basis, but to date, irrigation detection in passive microwave satellites has proven difficult. In this study, results show that the new enhanced soil moisture product from the Soil Moisture Active Passive satellite is able to capture irrigation signals over three semiarid regions in the western United States. This marks an advancement in Earth-observing satellite skill and the ability to monitor human impacts on the water cycle.

Experimental Sea Slicks in the Marsen (Maritime Remote Sensing) Exercise.

DTIC Science & Technology

1980-10-30

Experimental slicks with various surface properties were generated in the North Sea as part of the MARSEN (Maritime Remote Sensing ) exercise. The one...with remote sensing instrumentation. Because of the numerous effects of surface films on air-sea interfacial processes, these experiments were designed...information was obtained on the influence of sea surface films on the interpretation of signals received by remote sensing systems. Criteria for the

SYMPOSIUM ON REMOTE SENSING IN THE POLAR REGIONS

DTIC Science & Technology

The Arctic Institute of North America long has been interested in encouraging full and specific attention to applications of remote sensing to polar...research problems. The major purpose of the symposium was to acquaint scientists and technicians concerned with remote sensing with some of the...special problems of the polar areas and, in turn, to acquaint polar scientists with the potential of the use of remote sensing . The Symposium therefore was

REMOTE SENSING IN OCEANOGRAPHY.

DTIC Science & Technology

remote sensing from satellites. Sensing of oceanographic variables from aircraft began with the photographing of waves and ice. Since then remote measurement of sea surface temperatures and wave heights have become routine. Sensors tested for oceanographic applications include multi-band color cameras, radar scatterometers, infrared spectrometers and scanners, passive microwave radiometers, and radar imagers. Remote sensing has found its greatest application in providing rapid coverage of large oceanographic areas for synoptic and analysis and

Methods of Determining Playa Surface Conditions Using Remote Sensing

DTIC Science & Technology

1987-10-08

NO. 11. TITLE (include Security Classification) METHODS OF DETERMINING PLAYA SURFACE CONDITIONS USING REMOTE SENSING 12. PERSONAL AUTHOR(S) J. PONDER...PLAYA SURFACE CONDITIONS USING REMOTE SENSING J. Ponder Henley U. S. Army Engineer Topographic Laboratories Fort Belvoir, Virginia 22060-5546 "ABSTRACT...geochemistry, hydrology and remote sensing but all of these are important to the understanding of these unique geomorphic features. There is a large body

Needs Assessment for the Use of NASA Remote Sensing Data in the Development and Implementation of Estuarine and Coastal Water Quality Standards

NASA Technical Reports Server (NTRS)

Spiering, Bruce; Underwood, Lauren; Ellis, Chris; Lehrter, John; Hagy, Jim; Schaeffer, Blake

2010-01-01

The goals of the project are to provide information from satellite remote sensing to support numeric nutrient criteria development and to determine data processing methods and data quality requirements to support nutrient criteria development and implementation. The approach is to identify water quality indicators that are used by decision makers to assess water quality and that are related to optical properties of the water; to develop remotely sensed data products based on algorithms relating remote sensing imagery to field-based observations of indicator values; to develop methods to assess estuarine water quality, including trends, spatial and temporal variability, and seasonality; and to develop tools to assist in the development and implementation of estuarine and coastal nutrient criteria. Additional slides present process, criteria development, typical data sources and analyses for criteria process, the power of remote sensing data for the process, examples from Pensacola Bay, spatial and temporal variability, pixel matchups, remote sensing validation, remote sensing in coastal waters, requirements for remotely sensed data products, and needs assessment. An additional presentation examines group engagement and information collection. Topics include needs assessment purpose and objectives, understanding water quality decision making, determining information requirements, and next steps.

Commercial use of remote sensing in agriculture: a case study

NASA Astrophysics Data System (ADS)

Gnauck, Gary E.

1999-12-01

Over 25 years of research have clearly shown that an analysis of remote sensing imagery can provide information on agricultural crops. Most of this research has been funded by and directed toward the needs of government agencies. Commercial use of agricultural remote sensing has been limited to very small-scale operations supplying remote sensing services to a few selected customers. Datron/Transco Inc. undertook an internally funded remote sensing program directed toward the California cash crop industry (strawberries, lettuce, tomatoes, other fresh vegetables and cotton). The objectives of this program were twofold: (1) to assess the need and readiness of agricultural land managers to adopt remote sensing as a management tool, and (2) determine what technical barriers exist to large-scale implementation of this technology on a commercial basis. The program was divided into three phases: Planning, Engineering Test and Evaluation, and Commercial Operations. Findings: Remote sensing technology can deliver high resolution multispectral imagery with rapid turnaround, that can provide information on crop stress insects, disease and various soil parameters. The limiting factors to the use of remote sensing in agriculture are a lack of familiarization by the land managers, difficulty in translating 'information' into increased revenue or reduced cost for the land manager, and the large economies of scale needed to make the venture commercially viable.

Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations.

NASA Astrophysics Data System (ADS)

López López, Patricia; Wanders, Niko; Sutanudjaja, Edwin; Renzullo, Luigi; Sterk, Geert; Schellekens, Jaap; Bierkens, Marc

2015-04-01

The coarse spatial resolution of global hydrological models (typically > 0.25o) often limits their ability to resolve key water balance processes for many river basins and thus compromises their suitability for water resources management, especially when compared to locally-tunes river models. A possible solution to the problem may be to drive the coarse resolution models with high-resolution meteorological data as well as to assimilate ground-based and remotely-sensed observations of key water cycle variables. While this would improve the modelling resolution of the global model, the impact of prediction accuracy remains largely an open question. In this study we investigated the impact that assimilating streamflow and satellite soil moisture observations have on global hydrological model estimation, driven by coarse- and high-resolution meteorological observations, for the Murrumbidgee river basin in Australia. The PCR-GLOBWB global hydrological model is forced with downscaled global climatological data (from 0.5o downscaled to 0.1o resolution) obtained from the WATCH Forcing Data (WFDEI) and local high resolution gauging station based gridded datasets (0.05o), sourced from the Australian Bureau of Meteorology. Downscaled satellite derived soil moisture (from 0.5o downscaled to 0.1o resolution) from AMSR-E and streamflow observations collected from 25 gauging stations are assimilated using an ensemble Kalman filter. Several scenarios are analysed to explore the added value of data assimilation considering both local and global climatological data. Results show that the assimilation of streamflow observations result in the largest improvement of the model estimates. The joint assimilation of both streamflow and downscaled soil moisture observations leads to further improved in streamflow simulations (10% reduction in RMSE), mainly in the headwater catchments (up to 10,000 km2). Results also show that the added contribution of data assimilation, for both soil moisture and streamflow, is more pronounced when the global meteorological data are used to force the models. This is caused by the higher uncertainty and coarser resolution of the global forcing. This study demonstrates that it is possible to improve hydrological simulations forced by coarse resolution meteorological data with downscaled satellite soil moisture and streamflow observations and bring them closer to a hydrological model forced with local climatological data. These findings are important in light of the efforts that are currently done to go to global hyper-resolution modelling and can significantly help to advance this research.

Assimilation of CryoSat-2 altimetry to a hydrodynamic model of the Brahmaputra river

NASA Astrophysics Data System (ADS)

Schneider, Raphael; Nygaard Godiksen, Peter; Ridler, Marc-Etienne; Madsen, Henrik; Bauer-Gottwein, Peter

2016-04-01

Remote sensing provides valuable data for parameterization and updating of hydrological models, for example water level measurements of inland water bodies from satellite radar altimeters. Satellite altimetry data from repeat-orbit missions such as Envisat, ERS or Jason has been used in many studies, also synthetic wide-swath altimetry data as expected from the SWOT mission. This study is one of the first hydrologic applications of altimetry data from a drifting orbit satellite mission, namely CryoSat-2. CryoSat-2 is equipped with the SIRAL instrument, a new type of radar altimeter similar to SRAL on Sentinel-3. CryoSat-2 SARIn level 2 data is used to improve a 1D hydrodynamic model of the Brahmaputra river basin in South Asia set up in the DHI MIKE 11 software. CryoSat-2 water levels were extracted over river masks derived from Landsat imagery. After discharge calibration, simulated water levels were fitted to the CryoSat-2 data along the Assam valley by adapting cross section shapes and datums. The resulting hydrodynamic model shows accurate spatio-temporal representation of water levels, which is a prerequisite for real-time model updating by assimilation of CryoSat-2 altimetry or multi-mission data in general. For this task, a data assimilation framework has been developed and linked with the MIKE 11 model. It is a flexible framework that can assimilate water level data which are arbitrarily distributed in time and space. Different types of error models, data assimilation methods, etc. can easily be used and tested. Furthermore, it is not only possible to update the water level of the hydrodynamic model, but also the states of the rainfall-runoff models providing the forcing of the hydrodynamic model. The setup has been used to assimilate CryoSat-2 observations over the Assam valley for the years 2010 to 2013. Different data assimilation methods and localizations were tested, together with different model error representations. Furthermore, the impact of different filtering and clustering methods and error descriptions of the CryoSat-2 observations was evaluated. Performance improvement in terms of discharge and water level forecast due to the assimilation of satellite altimetry data was then evaluated. The model forecasts were also compared to climatology and persistence forecasts. Using ensemble based filters, the evaluation was done not only based on performance criteria for the central forecast such as root-mean-square error (RMSE) and Nash-Sutcliffe model efficiency (NSE), but also based on sharpness, reliability and continuous ranked probability score (CRPS) of the ensemble of probabilistic forecasts.

15 CFR 960.1 - Purpose.

Code of Federal Regulations, 2013 CFR

2013-01-01

... remote sensing satellite industry. (Available from NOAA, National Environmental Satellite Data and... LICENSING OF PRIVATE REMOTE SENSING SYSTEMS General § 960.1 Purpose. (a) The regulations in this part set... sensing space system under Title II of the Land Remote Sensing Policy Act of 1992 (15 U.S.C. 5601 et seq...

15 CFR 960.1 - Purpose.

Code of Federal Regulations, 2011 CFR

2011-01-01

... remote sensing satellite industry. (Available from NOAA, National Environmental Satellite Data and... LICENSING OF PRIVATE REMOTE SENSING SYSTEMS General § 960.1 Purpose. (a) The regulations in this part set... sensing space system under Title II of the Land Remote Sensing Policy Act of 1992 (15 U.S.C. 5601 et seq...

15 CFR 960.1 - Purpose.

Code of Federal Regulations, 2014 CFR

2014-01-01

... remote sensing satellite industry. (Available from NOAA, National Environmental Satellite Data and... LICENSING OF PRIVATE REMOTE SENSING SYSTEMS General § 960.1 Purpose. (a) The regulations in this part set... sensing space system under Title II of the Land Remote Sensing Policy Act of 1992 (15 U.S.C. 5601 et seq...

15 CFR 960.1 - Purpose.

Code of Federal Regulations, 2012 CFR

2012-01-01

... remote sensing satellite industry. (Available from NOAA, National Environmental Satellite Data and... LICENSING OF PRIVATE REMOTE SENSING SYSTEMS General § 960.1 Purpose. (a) The regulations in this part set... sensing space system under Title II of the Land Remote Sensing Policy Act of 1992 (15 U.S.C. 5601 et seq...

15 CFR 960.1 - Purpose.

Code of Federal Regulations, 2010 CFR

2010-01-01

... LICENSING OF PRIVATE REMOTE SENSING SYSTEMS General § 960.1 Purpose. (a) The regulations in this part set... sensing space system under Title II of the Land Remote Sensing Policy Act of 1992 (15 U.S.C. 5601 et seq... remote sensing satellite industry. (Available from NOAA, National Environmental Satellite Data and...

Advanced Remote Sensing Research

USGS Publications Warehouse

Slonecker, Terrence; Jones, John W.; Price, Susan D.; Hogan, Dianna

2008-01-01

'Remote sensing' is a generic term for monitoring techniques that collect information without being in physical contact with the object of study. Overhead imagery from aircraft and satellite sensors provides the most common form of remotely sensed data and records the interaction of electromagnetic energy (usually visible light) with matter, such as the Earth's surface. Remotely sensed data are fundamental to geographic science. The Eastern Geographic Science Center (EGSC) of the U.S. Geological Survey (USGS) is currently conducting and promoting the research and development of three different aspects of remote sensing science: spectral analysis, automated orthorectification of historical imagery, and long wave infrared (LWIR) polarimetric imagery (PI).

Remote sensing in the coastal and marine environment. Proceedings of the US North Atlantic Regional Workshop

NASA Technical Reports Server (NTRS)

Zaitzeff, J. B. (Editor); Cornillon, P. (Editor); Aubrey, D. A. (Editor)

1980-01-01

Presentations were grouped in the following categories: (1) a technical orientation of Earth resources remote sensing including data sources and processing; (2) a review of the present status of remote sensing technology applicable to the coastal and marine environment; (3) a description of data and information needs of selected coastal and marine activities; and (4) an outline of plans for marine monitoring systems for the east coast and a concept for an east coast remote sensing facility. Also discussed were user needs and remote sensing potentials in the areas of coastal processes and management, commercial and recreational fisheries, and marine physical processes.

Remote sensing of Earth terrain

NASA Technical Reports Server (NTRS)

Kong, J. A.

1992-01-01

Research findings are summarized for projects dealing with the following: application of theoretical models to active and passive remote sensing of saline ice; radiative transfer theory for polarimetric remote sensing of pine forest; scattering of electromagnetic waves from a dense medium consisting of correlated Mie scatterers with size distribution and applications to dry snow; variance of phase fluctuations of waves propagating through a random medium; theoretical modeling for passive microwave remote sensing of earth terrain; polarimetric signatures of a canopy of dielectric cylinders based on first and second order vector radiative transfer theory; branching model for vegetation; polarimetric passive remote sensing of periodic surfaces; composite volume and surface scattering model; and radar image classification.

Brazil's remote sensing activities in the Eighties

NASA Technical Reports Server (NTRS)

Raupp, M. A.; Pereiradacunha, R.; Novaes, R. A.

1985-01-01

Most of the remote sensing activities in Brazil have been conducted by the Institute for Space Research (INPE). This report describes briefly INPE's activities in remote sensing in the last years. INPE has been engaged in research (e.g., radiance studies), development (e.g., CCD-scanners, image processing devices) and applications (e.g., crop survey, land use, mineral resources, etc.) of remote sensing. INPE is also responsible for the operation (data reception and processing) of the LANDSATs and meteorological satellites. Data acquisition activities include the development of CCD-Camera to be deployed on board the space shuttle and the construction of a remote sensing satellite.

Application of remote sensing to state and regional problems. [for Mississippi

NASA Technical Reports Server (NTRS)

Miller, W. F.; Bouchillon, C. W.; Harris, J. C.; Carter, B.; Whisler, F. D.; Robinette, R.

1974-01-01

The primary purpose of the remote sensing applications program is for various members of the university community to participate in activities that improve the effective communication between the scientific community engaged in remote sensing research and development and the potential users of modern remote sensing technology. Activities of this program are assisting the State of Mississippi in recognizing and solving its environmental, resource and socio-economic problems through inventory, analysis, and monitoring by appropriate remote sensing systems. Objectives, accomplishments, and current status of the following individual projects are reported: (1) bark beetle project; (2) state park location planning; and (3) waste source location and stream channel geometry monitoring.

Physics teaching by infrared remote sensing of vegetation

NASA Astrophysics Data System (ADS)

Schüttler, Tobias; Maman, Shimrit; Girwidz, Raimund

2018-05-01

Context- and project-based teaching has proven to foster different affective and cognitive aspects of learning. As a versatile and multidisciplinary scientific research area with diverse applications for everyday life, satellite remote sensing is an interesting context for physics education. In this paper we give a brief overview of satellite remote sensing of vegetation and how to obtain your own, individual infrared remote sensing data with affordable converted digital cameras. This novel technique provides the opportunity to conduct individual remote sensing measurement projects with students in their respective environment. The data can be compared to real satellite data and is of sufficient accuracy for educational purposes.

Application of remote sensing to water resources problems

NASA Technical Reports Server (NTRS)

Clapp, J. L.

1972-01-01

The following conclusions were reached concerning the applications of remote sensing to water resources problems: (1) Remote sensing methods provide the most practical method of obtaining data for many water resources problems; (2) the multi-disciplinary approach is essential to the effective application of remote sensing to water resource problems; (3) there is a correlation between the amount of suspended solids in an effluent discharged into a water body and reflected energy; (4) remote sensing provides for more effective and accurate monitoring, discovery and characterization of the mixing zone of effluent discharged into a receiving water body; and (5) it is possible to differentiate between blue and blue-green algae.

SUPERFUND REMOTE SENSING SUPPORT

EPA Science Inventory

This task provides remote sensing technical support to the Superfund program. Support includes the collection, processing, and analysis of remote sensing data to characterize hazardous waste disposal sites and their history. Image analysis reports, aerial photographs, and assoc...

Remote Sensing and the Earth

NASA Technical Reports Server (NTRS)

Brosius, C. A.; Gervin, J. C.; Ragusa, J. M.

1977-01-01

A text book on remote sensing, as part of the earth resources Skylab programs, is presented. The fundamentals of remote sensing and its application to agriculture, land use, geology, water and marine resources, and environmental monitoring are summarized.

Operational Use of Remote Sensing within USDA

NASA Technical Reports Server (NTRS)

Bethel, Glenn R.

2007-01-01

A viewgraph presentation of remote sensing imagery within the USDA is shown. USDA Aerial Photography, Digital Sensors, Hurricane imagery, Remote Sensing Sources, Satellites used by Foreign Agricultural Service, Landsat Acquisitions, and Aerial Acquisitions are also shown.

Investigation related to multispectral imaging systems

NASA Technical Reports Server (NTRS)

Nalepka, R. F.; Erickson, J. D.

1974-01-01

A summary of technical progress made during a five year research program directed toward the development of operational information systems based on multispectral sensing and the use of these systems in earth-resource survey applications is presented. Efforts were undertaken during this program to: (1) improve the basic understanding of the many facets of multispectral remote sensing, (2) develop methods for improving the accuracy of information generated by remote sensing systems, (3) improve the efficiency of data processing and information extraction techniques to enhance the cost-effectiveness of remote sensing systems, (4) investigate additional problems having potential remote sensing solutions, and (5) apply the existing and developing technology for specific users and document and transfer that technology to the remote sensing community.

An object-based storage model for distributed remote sensing images

NASA Astrophysics Data System (ADS)

Yu, Zhanwu; Li, Zhongmin; Zheng, Sheng

2006-10-01

It is very difficult to design an integrated storage solution for distributed remote sensing images to offer high performance network storage services and secure data sharing across platforms using current network storage models such as direct attached storage, network attached storage and storage area network. Object-based storage, as new generation network storage technology emerged recently, separates the data path, the control path and the management path, which solves the bottleneck problem of metadata existed in traditional storage models, and has the characteristics of parallel data access, data sharing across platforms, intelligence of storage devices and security of data access. We use the object-based storage in the storage management of remote sensing images to construct an object-based storage model for distributed remote sensing images. In the storage model, remote sensing images are organized as remote sensing objects stored in the object-based storage devices. According to the storage model, we present the architecture of a distributed remote sensing images application system based on object-based storage, and give some test results about the write performance comparison of traditional network storage model and object-based storage model.

Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features.

PubMed

Li, Linyi; Xu, Tingbao; Chen, Yun

2017-01-01

In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images.

Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features

PubMed Central

Xu, Tingbao; Chen, Yun

2017-01-01

In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images. PMID:28761440

A Plane Target Detection Algorithm in Remote Sensing Images based on Deep Learning Network Technology

NASA Astrophysics Data System (ADS)

Shuxin, Li; Zhilong, Zhang; Biao, Li

2018-01-01

Plane is an important target category in remote sensing targets and it is of great value to detect the plane targets automatically. As remote imaging technology developing continuously, the resolution of the remote sensing image has been very high and we can get more detailed information for detecting the remote sensing targets automatically. Deep learning network technology is the most advanced technology in image target detection and recognition, which provided great performance improvement in the field of target detection and recognition in the everyday scenes. We combined the technology with the application in the remote sensing target detection and proposed an algorithm with end to end deep network, which can learn from the remote sensing images to detect the targets in the new images automatically and robustly. Our experiments shows that the algorithm can capture the feature information of the plane target and has better performance in target detection with the old methods.

Discharge Estimation in Ungauged Basins Through Variational Data Assimilation: The Potential of the SWOT Mission

NASA Astrophysics Data System (ADS)

Oubanas, H.; Gejadze, I.; Malaterre, P.-O.; Durand, M.; Wei, R.; Frasson, R. P. M.; Domeneghetti, A.

2018-03-01

Space-borne instruments can measure river water surface elevation, slope, and width. Remote sensing of river discharge in ungauged basins is far more challenging, however. This work investigates the estimation of river discharge from simulated observations of the forthcoming Surface Water and Ocean Topography (SWOT) satellite mission using a variant of the classical variational data assimilation method "4D-Var." The variational assimilation scheme simultaneously estimates discharge, river bathymetry, and bed roughness in the context of a 1.5 D full Saint-Venant hydraulic model. Algorithms and procedures are developed to apply the method to fully ungauged basins. The method was tested on the Po and Sacramento Rivers. The SWOT hydrology simulator was used to produce synthetic SWOT observations at each overpass time by simulating the interaction of SWOT radar measurements with the river water surface and nearby land surface topography at a scale of approximately 1 m, thus accounting for layover, thermal noise, and other effects. SWOT data products were synthesized by vectorizing the simulated radar returns, leading to height and width estimates at 200 m increments along the river centerlines. The ingestion of simulated SWOT data generally led to local improvements on prior bathymetry and roughness estimates which allowed the prediction of river discharge at the overpass times with relative root mean squared errors of 12.1% and 11.2% for the Po and Sacramento Rivers, respectively. Nevertheless, equifinality issues that arise from the simultaneous estimation of bed elevation and roughness may prevent their use for different applications, other than discharge estimation through the presented framework.

A Particle Batch Smoother Approach to Snow Water Equivalent Estimation

NASA Technical Reports Server (NTRS)

Margulis, Steven A.; Girotto, Manuela; Cortes, Gonzalo; Durand, Michael

2015-01-01

This paper presents a newly proposed data assimilation method for historical snow water equivalent SWE estimation using remotely sensed fractional snow-covered area fSCA. The newly proposed approach consists of a particle batch smoother (PBS), which is compared to a previously applied Kalman-based ensemble batch smoother (EnBS) approach. The methods were applied over the 27-yr Landsat 5 record at snow pillow and snow course in situ verification sites in the American River basin in the Sierra Nevada (United States). This basin is more densely vegetated and thus more challenging for SWE estimation than the previous applications of the EnBS. Both data assimilation methods provided significant improvement over the prior (modeling only) estimates, with both able to significantly reduce prior SWE biases. The prior RMSE values at the snow pillow and snow course sites were reduced by 68%-82% and 60%-68%, respectively, when applying the data assimilation methods. This result is encouraging for a basin like the American where the moderate to high forest cover will necessarily obscure more of the snow-covered ground surface than in previously examined, less-vegetated basins. The PBS generally outperformed the EnBS: for snow pillows the PBSRMSE was approx.54%of that seen in the EnBS, while for snow courses the PBSRMSE was approx.79%of the EnBS. Sensitivity tests show relative insensitivity for both the PBS and EnBS results to ensemble size and fSCA measurement error, but a higher sensitivity for the EnBS to the mean prior precipitation input, especially in the case where significant prior biases exist.

Progress in Modeling Global Atmospheric CO2 Fluxes and Transport: Results from Simulations with Diurnal Fluxes

NASA Technical Reports Server (NTRS)

Collatz, G. James; Kawa, R.

2007-01-01

Progress in better determining CO2 sources and sinks will almost certainly rely on utilization of more extensive and intensive CO2 and related observations including those from satellite remote sensing. Use of advanced data requires improved modeling and analysis capability. Under NASA Carbon Cycle Science support we seek to develop and integrate improved formulations for 1) atmospheric transport, 2) terrestrial uptake and release, 3) biomass and 4) fossil fuel burning, and 5) observational data analysis including inverse calculations. The transport modeling is based on meteorological data assimilation analysis from the Goddard Modeling and Assimilation Office. Use of assimilated met data enables model comparison to CO2 and other observations across a wide range of scales of variability. In this presentation we focus on the short end of the temporal variability spectrum: hourly to synoptic to seasonal. Using CO2 fluxes at varying temporal resolution from the SIB 2 and CASA biosphere models, we examine the model's ability to simulate CO2 variability in comparison to observations at different times, locations, and altitudes. We find that the model can resolve much of the variability in the observations, although there are limits imposed by vertical resolution of boundary layer processes. The influence of key process representations is inferred. The high degree of fidelity in these simulations leads us to anticipate incorporation of realtime, highly resolved observations into a multiscale carbon cycle analysis system that will begin to bridge the gap between top-down and bottom-up flux estimation, which is a primary focus of NACP.

A remote sensing and GIS-enabled asset management system (RS-GAMS).

DOT National Transportation Integrated Search

2013-04-01

Under U.S. Department of Transportation (DOT) Commercial Remote Sensing and : Spatial Information (CRS&SI) Technology Initiative 2 of the Transportation : Infrastructure Construction and Condition Assessment, an intelligent Remote Sensing and : GIS-b...

Remote Sensing.

ERIC Educational Resources Information Center

Williams, Richard S., Jr.; Southworth, C. Scott

1983-01-01

The Landsat Program became the major event of 1982 in geological remote sensing with the successful launch of Landsat 4. Other 1982 remote sensing accomplishments, research, publications, (including a set of Landsat worldwide reference system index maps), and conferences are highlighted. (JN)

Remote sensing utility in a disaster struck urban environment

NASA Technical Reports Server (NTRS)

Rush, M.; Holguin, A.; Vernon, S.

1974-01-01

A project to determine the ways in which remote sensing can contribute to solutions of urban public health problems in time of natural disaster is discussed. The objectives of the project are to determine and describe remote sensing standard operating procedures for public health assistance during disaster relief operations which will aid the agencies and organizations involved in disaster intervention. Proposed tests to determine the validity of the remote sensing system are reported.

Removal of Surface-Reflected Light for the Measurement of Remote-Sensing Reflectance from an Above-Surface Platform

DTIC Science & Technology

2010-12-06

raw data). To remove surface-reflected light in field measurements of remote sensing reflectance, a spectral optimization approach was applied, with...results compared with those from remote - sensing models and from direct measurements. The agreement from different determinations suggests that...reasonable results for remote sensing reflectance of clear blue water to turbid brown water are obtainable from above-surface measurements, even under conditions of high waves.

Bibliography of Remote Sensing Techniques Used in Wetland Research

DTIC Science & Technology

1993-01-01

8217 is investigating the application of remote sensing technology for detecting changes in wetland environments. This report documents a bibliographic...search conducted as part of that work unit on applications of remote sensing techniques in wetland research. Results were used to guide research...efforts on the use of remote sensing technology for wetland change detection and assessment. The citations are presented in three appendixes, organized by wetland type, sensor type, and author.

Use of Openly Available Satellite Images for Remote Sensing Education

NASA Astrophysics Data System (ADS)

Wang, C.-K.

2011-09-01

With the advent of Google Earth, Google Maps, and Microsoft Bing Maps, high resolution satellite imagery are becoming more easily accessible than ever. It have been the case that the college students may already have wealth experiences with the high resolution satellite imagery by using these software and web services prior to any formal remote sensing education. It is obvious that the remote sensing education should be adjusted to the fact that the audience are already the customers of remote sensing products (through the use of the above mentioned services). This paper reports the use of openly available satellite imagery in an introductory-level remote sensing course in the Department of Geomatics of National Cheng Kung University as a term project. From the experience learned from the fall of 2009 and 2010, it shows that this term project has effectively aroused the students' enthusiastic toward Remote Sensing.

Strategies for using remotely sensed data in hydrologic models

NASA Technical Reports Server (NTRS)

Peck, E. L.; Keefer, T. N.; Johnson, E. R. (Principal Investigator)

1981-01-01

Present and planned remote sensing capabilities were evaluated. The usefulness of six remote sensing capabilities (soil moisture, land cover, impervious area, areal extent of snow cover, areal extent of frozen ground, and water equivalent of the snow cover) with seven hydrologic models (API, CREAMS, NWSRFS, STORM, STANFORD, SSARR, and NWSRFS Snowmelt) were reviewed. The results indicate remote sensing information has only limited value for use with the hydrologic models in their present form. With minor modifications to the models the usefulness would be enhanced. Specific recommendations are made for incorporating snow covered area measurements in the NWSRFS Snowmelt model. Recommendations are also made for incorporating soil moisture measurements in NWSRFS. Suggestions are made for incorporating snow covered area, soil moisture, and others in STORM and SSARR. General characteristics of a hydrologic model needed to make maximum use of remotely sensed data are discussed. Suggested goals for improvements in remote sensing for use in models are also established.

A preliminary study of the statistical analyses and sampling strategies associated with the integration of remote sensing capabilities into the current agricultural crop forecasting system

NASA Technical Reports Server (NTRS)

Sand, F.; Christie, R.

1975-01-01

Extending the crop survey application of remote sensing from small experimental regions to state and national levels requires that a sample of agricultural fields be chosen for remote sensing of crop acreage, and that a statistical estimate be formulated with measurable characteristics. The critical requirements for the success of the application are reviewed in this report. The problem of sampling in the presence of cloud cover is discussed. Integration of remotely sensed information about crops into current agricultural crop forecasting systems is treated on the basis of the USDA multiple frame survey concepts, with an assumed addition of a new frame derived from remote sensing. Evolution of a crop forecasting system which utilizes LANDSAT and future remote sensing systems is projected for the 1975-1990 time frame.

Archimedean Witness: The Application of Remote Sensing as an Aid to Human Rights Prosecutions

NASA Astrophysics Data System (ADS)

Walker, James Robin

The 21st century has seen a significant increase in the use of remote sensing technology in the international human rights arena for the purposes of documenting crimes against humanity. The nexus between remote sensing, human rights activism, and international criminal prosecutions sits at a significant crossroads within geographic thought, calling attention to the epistemological and geopolitical implications that stem from the "view from nowhere" afforded by satellite imagery. Therefore, this thesis is divided into three sections. The first looks at the geographical questions raised by the expansion of remote sensing use in the context of international activism. The second explores the complications inherent in the presentation of remote sensing data as evidence of war crimes. Building upon the first two, the third section is a case study in alternate forms of analysis, aimed at expanding the utility of remote sensing data in international criminal prosecutions.

[Small unmanned aerial vehicles for low-altitude remote sensing and its application progress in ecology.

PubMed

Sun, Zhong Yu; Chen, Yan Qiao; Yang, Long; Tang, Guang Liang; Yuan, Shao Xiong; Lin, Zhi Wen

2017-02-01

Low-altitude unmanned aerial vehicles (UAV) remote sensing system overcomes the deficiencies of space and aerial remote sensing system in resolution, revisit period, cloud cover and cost, which provides a novel method for ecological research on mesoscale. This study introduced the composition of UAV remote sensing system, reviewed its applications in species, population, community and ecosystem ecology research. Challenges and opportunities of UAV ecology were identified to direct future research. The promising research area of UAV ecology includes the establishment of species morphology and spectral characteristic data base, species automatic identification, the revelation of relationship between spectral index and plant physiological processes, three-dimension monitoring of ecosystem, and the integration of remote sensing data from multi resources and multi scales. With the development of UAV platform, data transformation and sensors, UAV remote sensing technology will have wide application in ecology research.

International Models and Methods of Remote Sensing Education and Training.

ERIC Educational Resources Information Center

Anderson, Paul S.

A classification of remote sensing courses throughout the world, the world-wide need for sensing instruction, and alternative instructional methods for meeting those needs are discussed. Remote sensing involves aerial photointerpretation or the use of satellite and other non-photographic imagery; its focus is to interpret what is in the photograph…

A Review and Analysis of Remote Sensing Capability for Air Quality Measurements as a Potential Decision Support Tool Conducted by the NASA DEVELOP Program

NASA Technical Reports Server (NTRS)

Ross, A.; Richards, A.; Keith, K.; Frew, C.; Boseck, J.; Sutton, S.; Watts, C.; Rickman, D.

2007-01-01

This project focused on a comprehensive utilization of air quality model products as decision support tools (DST) needed for public health applications. A review of past and future air quality measurement methods and their uncertainty, along with the relationship of air quality to national and global public health, is vital. This project described current and future NASA satellite remote sensing and ground sensing capabilities and the potential for using these sensors to enhance the prediction, prevention, and control of public health effects that result from poor air quality. The qualitative uncertainty of current satellite remotely sensed air quality, the ground-based remotely sensed air quality, the air quality/public health model, and the decision making process is evaluated in this study. Current peer-reviewed literature suggests that remotely sensed air quality parameters correlate well with ground-based sensor data. A satellite remote-sensed and ground-sensed data complement is needed to enhance the models/tools used by policy makers for the protection of national and global public health communities

Deriving the Characteristic Scale for Effectively Monitoring Heavy Metal Stress in Rice by Assimilation of GF-1 Data with the WOFOST Model

PubMed Central

Huang, Zhi; Liu, Xiangnan; Jin, Ming; Ding, Chao; Jiang, Jiale; Wu, Ling

2016-01-01

Accurate monitoring of heavy metal stress in crops is of great importance to assure agricultural productivity and food security, and remote sensing is an effective tool to address this problem. However, given that Earth observation instruments provide data at multiple scales, the choice of scale for use in such monitoring is challenging. This study focused on identifying the characteristic scale for effectively monitoring heavy metal stress in rice using the dry weight of roots (WRT) as the representative characteristic, which was obtained by assimilation of GF-1 data with the World Food Studies (WOFOST) model. We explored and quantified the effect of the important state variable LAI (leaf area index) at various spatial scales on the simulated rice WRT to find the critical scale for heavy metal stress monitoring using the statistical characteristics. Furthermore, a ratio analysis based on the varied heavy metal stress levels was conducted to identify the characteristic scale. Results indicated that the critical threshold for investigating the rice WRT in monitoring studies of heavy metal stress was larger than 64 m but smaller than 256 m. This finding represents a useful guideline for choosing the most appropriate imagery. PMID:26959033

Improving Regional Forecast by Assimilating Atmospheric InfraRed Sounder (AIRS) Profiles into WRF Model

NASA Technical Reports Server (NTRS)

Chou, Shih-Hung; Zavodsky, Brad; Jedlovec, Gary J.

2009-01-01

In data sparse regions, remotely-sensed observations can be used to improve analyses and produce improved forecasts. One such source comes from the Atmospheric InfraRed Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), represents one of the most advanced space-based atmospheric sounding systems. The purpose of this paper is to describe a procedure to optimally assimilate high resolution AIRS profile data into a regional configuration of the Advanced Research WRF (ARW) version 2.2 using WRF-Var. The paper focuses on development of background error covariances for the regional domain and background type, and an optimal methodology for ingesting AIRS temperature and moisture profiles as separate overland and overwater retrievals with different error characteristics. The AIRS thermodynamic profiles are derived from the version 5.0 Earth Observing System (EOS) science team retrieval algorithm and contain information about the quality of each temperature layer. The quality indicators were used to select the highest quality temperature and moisture data for each profile location and pressure level. The analyses were then used to conduct a month-long series of regional forecasts over the continental U.S. The long-term impacts of AIRS profiles on forecast were assessed against verifying NAM analyses and stage IV precipitation data.

Validation of reactive gases and aerosols in the MACC global analysis and forecast system

NASA Astrophysics Data System (ADS)

Eskes, H.; Huijnen, V.; Arola, A.; Benedictow, A.; Blechschmidt, A.-M.; Botek, E.; Boucher, O.; Bouarar, I.; Chabrillat, S.; Cuevas, E.; Engelen, R.; Flentje, H.; Gaudel, A.; Griesfeller, J.; Jones, L.; Kapsomenakis, J.; Katragkou, E.; Kinne, S.; Langerock, B.; Razinger, M.; Richter, A.; Schultz, M.; Schulz, M.; Sudarchikova, N.; Thouret, V.; Vrekoussis, M.; Wagner, A.; Zerefos, C.

2015-11-01

The European MACC (Monitoring Atmospheric Composition and Climate) project is preparing the operational Copernicus Atmosphere Monitoring Service (CAMS), one of the services of the European Copernicus Programme on Earth observation and environmental services. MACC uses data assimilation to combine in situ and remote sensing observations with global and regional models of atmospheric reactive gases, aerosols, and greenhouse gases, and is based on the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecasts (ECMWF). The global component of the MACC service has a dedicated validation activity to document the quality of the atmospheric composition products. In this paper we discuss the approach to validation that has been developed over the past 3 years. Topics discussed are the validation requirements, the operational aspects, the measurement data sets used, the structure of the validation reports, the models and assimilation systems validated, the procedure to introduce new upgrades, and the scoring methods. One specific target of the MACC system concerns forecasting special events with high-pollution concentrations. Such events receive extra attention in the validation process. Finally, a summary is provided of the results from the validation of the latest set of daily global analysis and forecast products from the MACC system reported in November 2014.

Comparison of Tropical Ozone from SHADOZ with Remote Sensing Retrievals from Suomi-npp Ozone Mapping Profile Suite (OMPS)

NASA Technical Reports Server (NTRS)

Witte, Jacquelyn C.; Thompson, Anne M.; Ziemke, Jerald R.; Wargan, Krzysztof

2014-01-01

The Ozone Mapping Profile Suite (OMPS) was launched October 28, 2011 on-board the Suomi NPP satellite (http://npp.gsfc.nasa.gov). OMPS is the next generation total column ozone mapping instrument for monitoring the global distribution of stratospheric ozone. OMPS includes a limb profiler to measure the vertical structure of stratosphere ozone down to the mid-troposphere. This study uses tropical ozonesonde profile measurements from the Southern Hemisphere Additional Ozonesondes (SHADOZ, http://croc.gsfc.nasa.gov/shadoz) archive to evaluate total column ozone retrievals from OMPS and concurrent measurements from the Aura Ozone Monitoring Instrument (OMI), the predecessor of OMPS with a data record going back to 2004. We include ten SHADOZ stations that contain data overlapping the OMPS time period (2012-2013). This study capitalizes on the ozone profile measurements from SHADOZ to evaluate OMPS limb profile retrievals. Finally, we use SHADOZ sondes and OMPS retrievals to examine the agreement with the GEOS-5 Ozone Assimilation System (GOAS). The GOAS uses data from the OMI and the Microwave Limb Sounder (MLS) to constrain the total column and stratospheric profiles of ozone. The most recent version of the assimilation system is well constrained to the total column compared with SHADOZ ozonesonde data.

Environmental controls of daytime leaf carbon exchange: Implications for estimates of ecosystem fluxes in a deciduous forest

NASA Astrophysics Data System (ADS)

Heskel, M.; Tang, J.

2017-12-01

Leaf-level photosynthesis and respiration are sensitive to short- and long-term changed in temperature, and how these processes respond to phenological and seasonal transitions and daily temperature variation dictate how carbon is first assimilated and released in terrestrial ecosystems. We examined the short-term temperature response of daytime leaf carbon exchange at Harvard Forest across growing season, with the specific objective to quantify the light inhibition of dark respiration and photorespiration in leaves and use this to better inform daytime carbon assimilation and efflux estimates at the canopy scale. Dark and light respiration increased with measurement temperature and varied seasonally in a proportional manner, with the level of inhibition remaining relatively constant through the growing season. Higher rates of mitochondrial respiration and photorespiration at warmer temperatures drove a lower carbon use efficiency. Using temperature, light, and canopy leaf area index values to drive models, we estimate partitioned ecosystem fluxes and re-calculate gross primary production under multiple scenarios that include and exclude the impact of light inhibition, thermal acclimation, and seasonal variation in physiology. Quantifying the contribution of these `small fluxes' to ecosystem carbon exchange in forests provides a nuanced approach for integrating physiology into regional model estimates derived from eddy covariance and remote-sensing methods.

Theme section for 36th International Symposium for Remote Sensing of the Environment in Berlin

NASA Astrophysics Data System (ADS)

Trinder, John; Waske, Björn

2016-09-01

The International Symposium for Remote Sensing of the Environment (ISRSE) is the longest series of international conferences held on the topic of Remote Sensing, commencing in Ann Arbor, Michigan USA in 1962. While the name of the conference has changed over the years, it is regularly held approximately every 2 years and continues to be one of the leading international conferences on remote sensing. The latest of these conferences, the 36th ISRSE, was held in Berlin, Germany from 11 to 15 May 2015. All complete papers from the conference are available in the ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences at http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W3/index.html.

Measurement of Ecosystem Metabolism across Climatic and Vegetation Gradients in California

NASA Astrophysics Data System (ADS)

DuBois, S.; Serbin, S.; Desai, A. R.; Kruger, E.; Kingdon, C.; Goulden, M.; Townsend, P. A.

2013-12-01

Terrestrial ecosystem models require information on vegetation structure, phenology, demographics, biochemistry, radiation properties, and physiology in order to accurately simulate the responses of ecosystem functioning to global change and disturbances. These models generally depend on a small number of intensive, fine-scaled point-based measurements from eddy covariance towers, detailed vegetation surveys, literature values, and site-scale data assimilation techniques to improve model calibration. However, the limited geographic and/or temporal scope of measurements can lead to inadequate model generalizations of modeled carbon (C), water, and energy fluxes across broad regions and relevant time periods. Remote sensing approaches, particularly imaging spectroscopy (IS) and thermal infrared (TIR) data, have the potential to provide the broad-scale spatial and temporal dynamics in many important vegetation properties related to ecosystem functioning. As part of the ongoing NASA HyspIRI Airborne Campaign (http://hyspiri.jpl.nasa.gov/airborne) we are assessing the potential of IS+TIR to generate spatially explicit estimates of two important parameters characterizing plant photosynthetic capacity: the maximum rate of CO2 carboxylation by RuBisCo (Vcmax), and the maximum rate of electron transport required for the regeneration of RuBP needed in Calvin Cycle processes (Jmax). These estimates are based on recent evidence that both properties can be predicted at the leaf level using spectroscopy techniques (Ainsworth et al. 2013 [http://tinyurl.com/n5xnzjg]; Serbin et al. 2012 [http://tinyurl.com/mhocmlz]). It follows that estimation of these variables from remotely sensed IS+TIR (i.e. AVIRIS & MASTER) could facilitate the prediction of seasonal C assimilation across large areas using data from the anticipated HyspIRI satellite mission. Our research focuses on two climate-elevation transects in California, which span a vegetation gradient from coastal sage and chaparral to oak woodlands and closed-canopy coniferous forests, as well as agro-ecosystems located throughout the Central and Imperial Valleys. We are also comparing remotely sensed estimates of ecosystem photosynthetic capacity with C flux data from a series of 10 eddy covariance towers. Results from the 2013 field season highlight the large range in sampled vegetation structure, optical properties (i.e. reflectance and transmittance) and physiology (i.e. Vcmax, Jmax, and cholorphyll fluorescence). Using approaches similar to Serbin et al. (2012) we have confirmed the ability of spectroscopy to estimate Vcmax and Jmax across these diverse and structurally complex vegetation types. Ecosystem products, such as gross primary productivity, estimated from flux towers highlight the relationship between climatic parameters and vegetation productivity. Multiple data-years allow this relationship to be examined under various climatic forcings including drought and heat stress. Based on these preliminary results, our next step is to scale leaf-level information to AVIRIS footprints using radiative transfer and statistical modeling approaches with ecosystem modeling in order to assess the IS data products against flux tower observations.

Estimating national forest carbon stocks and dynamics: combining models and remotely sensed information

NASA Astrophysics Data System (ADS)

Smallman, Luke; Williams, Mathew

2016-04-01

Forests are a critical component of the global carbon cycle, storing significant amounts of carbon, split between living biomass and dead organic matter. The carbon budget of forests is the most uncertain component of the global carbon cycle - it is currently impossible to quantify accurately the carbon source/sink strength of forest biomes due to their heterogeneity and complex dynamics. It has been a major challenge to generate robust carbon budgets across landscapes due to data scarcity. Models have been used but outputs have lacked an assessment of uncertainty, making a robust assessment of their reliability and accuracy challenging. Here a Metropolis Hastings - Markov Chain Monte Carlo (MH-MCMC) data assimilation framework has been used to combine remotely sensed leaf area index (MODIS), biomass (where available) and deforestation estimates, in addition to forest planting and clear-felling information from the UK's national forest inventory, an estimate of soil carbon from the Harmonized World Database (HWSD) and plant trait information with a process model (DALEC) to produce a constrained analysis with a robust estimate of uncertainty of the UK forestry carbon budget between 2000 and 2010. Our analysis estimates the mean annual UK forest carbon sink at -3.9 MgC ha-1yr-1 with a 95 % confidence interval between -4.0 and -3.1 MgC ha-1 yr-1. The UK national forest inventory (NFI) estimates the mean UK forest carbon sink to be between -1.4 and -5.5 MgC ha-1 yr-1. The analysis estimate for total forest biomass stock in 2010 is estimated at 229 (177/232) TgC, while the NFI an estimated total forest biomass carbon stock of 216 TgC. Leaf carbon area (LCA) is a key plant trait which we are able to estimate using our analysis. Comparison of median estimates for LCA retrieved from the analysis and a UK land cover map show higher and lower values for LCA are estimated areas dominated by needle leaf and broad leaf forests forest respectively, consistent with ecological expectations. Moreover, the retrieved LCA is positively correlated with leaf-life span and negatively correlated with allocation of photosynthate to foliage, supported by field observations. This emergence of key plant traits and correlations between traits increases our confidence in the robustness of this analysis. Furthermore, this framework also allows us to search for additional emergent properties from the analysis such as spatial variation of retrieved drought tolerance. Finally our analysis is able to identify components of the carbon cycle with the largest uncertainty providing targets for future observations (e.g. remotely sensed biomass). Our Bayesian analysis system is ideally suited for assimilation of multiple biomass estimates and their associated uncertainties to reduce both uncertainty in the state of the system but also process parameters (e.g. wood residence time).

THE REMOTE SENSING DATA GATEWAY

EPA Science Inventory

The EPA Remote Sensing Data Gateway (RSDG) is a pilot project in the National Exposure Research Laboratory (NERL) to develop a comprehensive data search, acquisition, delivery and archive mechanism for internal, national and international sources of remote sensing data for the co...

A remote sensing and GIS-enabled asset management system (RS-GAMS) : phase 2.

DOT National Transportation Integrated Search

2014-04-01

Under the U.S. Department of Transportation (DOT) Commercial Remote Sensing and Spatial : Information (CRS&SI) Technology Initiative 2 of the Transportation Infrastructure Construction : and Condition Assessment, an intelligent Remote Sensing and GIS...

Remote sensing applications program

NASA Technical Reports Server (NTRS)

1984-01-01

The activities of the Mississippi Remote Sensing Center are described in addition to technology transfer and information dissemination, remote sensing topics such as timber identification, water quality, flood prevention, land use, erosion control, animal habitats, and environmental impact studies are also discussed.

Remote Sensing Terminology in a Global and Knowledge-Based World

NASA Astrophysics Data System (ADS)

Kancheva, Rumiana

The paper is devoted to terminology issues related to all aspects of remote sensing research and applications. Terminology is the basis for a better understanding among people. It is crucial to keep up with the latest developments and novelties of the terminology in advanced technology fields such as aerospace science and industry. This is especially true in remote sensing and geoinformatics which develop rapidly and have ever extending applications in various domains of science and human activities. Remote sensing terminology issues are directly relevant to the contemporary worldwide policies on information accessibility, dissemination and utilization of research results in support of solutions to global environmental challenges and sustainable development goals. Remote sensing and spatial information technologies are an integral part of the international strategies for cooperation in scientific, research and application areas with a particular accent on environmental monitoring, ecological problems natural resources management, climate modeling, weather forecasts, disaster mitigation and many others to which remote sensing data can be put. Remote sensing researchers, professionals, students and decision makers of different counties and nationalities should fully understand, interpret and translate into their native language any term, definition or acronym found in papers, books, proceedings, specifications, documentation, and etc. The importance of the correct use, precise definition and unification of remote sensing terms refers not only to people working in this field but also to experts in a variety of disciplines who handle remote sensing data and information products. In this paper, we draw the attention on the specifics, peculiarities and recent needs of compiling specialized dictionaries in the area of remote sensing focusing on Earth observations and the integration of remote sensing with other geoinformation technologies such as photogrammetry, geodesy, GIS, etc. Our belief is that the elaboration of bilingual and multilingual dictionaries and glossaries in this spreading, most technically advanced and promising field of human expertise is of great practical importance. The work on an English-Bulgarian Dictionary of Remote Sensing Terms is described including considerations on its scope, structure, information content, sellection of terms, and etc. The vision builds upon previous national and international experience and makes use of ongoing activities on the subject. Any interest in cooperation and initiating suchlike collaborative projects is welcome and highly appreciated.

Indicators of international remote sensing activities

NASA Technical Reports Server (NTRS)

Spann, G. W.

1977-01-01

The extent of worldwide remote sensing activities, including the use of satellite and high/medium altitude aircraft data was studied. Data were obtained from numerous individuals and organizations with international remote sensing responsibilities. Indicators were selected to evaluate the nature and scope of remote sensing activities in each country. These indicators ranged from attendance at remote sensing workshops and training courses to the establishment of earth resources satellite ground stations and plans for the launch of earth resources satellites. Results indicate that this technology constitutes a rapidly increasing component of environmental, land use, and natural resources investigations in many countries, and most of these countries rely on the LANDSAT satellites for a major portion of their data.