Search NASA⌕ Search

SEARCH · Search NASA

Results for “Land Surface Modeling”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

A Real-Time MODIS Vegetation Composite for Land Surface Models and Short-Term Forecasting

The NASA Short-term Prediction Research and Transition (SPoRT) Center is producing real-time, 1- km resolution Normalized Difference Vegetation Index (NDVI) gridded composites over a Continental U.S. domain. These composites are updated daily based on swath data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the polar orbiting NASA Aqua and Terra satellites, with a product time lag of about one day. A simple time-weighting algorithm is applied to the NDVI swath data that queries the previous 20 days of data to ensure a continuous grid of data populated at all pixels. The daily composites exhibited good continuity both spatially and temporally during June and July 2010. The composites also nicely depicted high greenness anomalies that resulted from significant rainfall over southwestern Texas, Mexico, and New Mexico during July due to early-season tropical cyclone activity. The SPoRT Center is in the process of computing greenness vegetation fraction (GVF) composites from the MODIS NDVI data at the same spatial and temporal resolution for use in the NASA Land Information System (LIS). The new daily GVF dataset would replace the monthly climatological GVF database (based on Advanced Very High Resolution Radiometer [AVHRR] observations from 1992-93) currently available to the Noah land surface model (LSM) in both LIS and the public version of the Weather Research and Forecasting (WRF) model. The much higher spatial resolution (1 km versus 0.15 degree) and daily updates based on real-time satellite observations have the capability to greatly improve the simulation of the surface energy budget in the Noah LSM within LIS and WRF. Once code is developed in LIS to incorporate the daily updated GVFs, the SPoRT Center will conduct simulation sensitivity experiments to quantify the impacts and improvements realized by the MODIS real-time GVF data. This presentation will describe the methodology used to develop the 1-km MODIS NDVI composites and show sample output from summer 2010, compare the MODIS GVF data to the AVHRR monthly climatology, and illustrate the sensitivity of the Noah LSM within LIS and/or the coupled LIS/WRF system to the new MODIS GVF dataset.

Case, Jonathan L.↗
NASA NTRS2021

Vegetation Monitoring Optimization with Normalized Difference Vegetation Index and Evapotranspiration Using Remote Sensing Measurements and Land Surface Models Over East Africa

The majority of people in East Africa rely on the agro-pastoral system for their livelihood, which is highly vulnerable to droughts and flooding. Agro-pastoral droughts are endemic to the region and are considered the main natural hazard that contributes to food insecurity. Drought begins with rainfall deficit, gradually leading to soil moisture deficit, higher land surface temperature, and finally impacts to vegetation growth. Therefore, monitoring vegetation conditions is essential in understanding the progression of drought, potential effects on food security, and providing early warning information needed for drought mitigation decisions. Because vegetation processes couple the land and atmosphere, monitoring of vegetation conditions requires consideration of both water provision and demand. While there is consensus in using either the Normalized Difference Vegetation Index (NDVI) or evapotranspiration(ET) for vegetation monitoring, a comprehensive assessment optimizing the use of both has not yet been done. Moreover, the evaluation methods for understanding the relationships between NDVI and ET for vegetation monitoring are also limited. Taking these gaps into account we have developed a framework to optimize vegetation monitoring using both NDVI and ET by identifying where they perform the best by using triple collocation and cross-correlation methods. We estimated the random error structure in Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI; ET from the Operational Simplified Surface Energy Balance (SSEBop) model; and ET from land surface models (LSMs). LSM ET and SSEBop ET have been found to be better indicators for vegetation monitoring during extreme drought events, while NDVI could provide better information on vegetation condition during wetter than normal conditions. The random error structures of these variables suggest that LSM ET is most likely to provide important information for vegetation monitoring over low and high ends of the vegetation fraction areas. Over moderate vegetative areas, any of these variables could provide important vegetation information for drought characterization and food security assessments. While this study provides a framework for optimizing vegetation monitoring for drought and food security assessments over East Africa, the framework can be adopted to optimize vegetation monitoring over any other drought and food insecure region of the world.

triple collocation↗
NASA NTRS2022

Evaluation of High Mountain Asia-Land Data Assimilation System (version 1) from 2003 to 2016: 2. The impact of assimilating satellite-based snow cover and freeze/thaw observations into a land surface model

This second paper of the two-part series focuses on demonstrating the impact of assimilating satellite-based snow cover and freeze/thaw observations into the hyper-resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region from 2003 to 2016. To this end, this study systematically evaluates a total of six sets of 0.01° (∼1 km) model simulations forced by different precipitation forcings, with and without the dual assimilation scheme enabled, at point-scale, basin-scale, and domain-scale. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near-surface soil temperature, snow depth, snow water equivalent (SWE), and total runoff. First, the point-scale assessment is mainly conducted via evaluating against ground-based measurements. In general, the assimilation enabled estimates are better than no-assimilation counterparts. Second, the basin-scale runoff assessment demonstrates that across three snow-dominated basins, the assimilation enabled experiment yields systematic improvements in all goodness-of-fit statistics through mitigating the negative effects brought by the fixed long-term precipitation correction factors. For example, when forced by the bias-corrected precipitation, the assimilation-enabled experiment improves the bias by 69%, the root-mean-squared error by 30%, and the unbiased root-mean-squared error by 18% (relative to the no-assimilation counterpart). Finally, the domainscale assessment is conducted via evaluating against satellite-based SWE and skin temperature products. Both sets of domain-scale analysis further corroborate the findings in the point-scale evaluations. Overall, this study suggests the benefits of the proposed multi-variate assimilation system in improving the cryospherichydrological process within a land surface model for use in HMA.

Yuan Xue↗
NASA NTRS2022

Evaluation of High Mountain Asia-Land Data Assimilation System (version 1) from 2003 to 2016: 2. The impact of assimilating satellite-based snow cover and freeze/thaw observations into a land surface model

This second paper of the two-part series focuses on demonstrating the impact of assimilating satellite-based snow cover and freeze/thaw observations into the hyper-resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region from 2003 to 2016. To this end, this study systematically evaluates a total of six sets of 0.01° (∼1 km) model simulations forced by different precipitation forcings, with and without the dual assimilation scheme enabled, at point-scale, basin-scale, and domain-scale. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near-surface soil temperature, snow depth, snow water equivalent (SWE), and total runoff. First, the point-scale assessment is mainly conducted via evaluating against ground-based measurements. In general, the assimilation enabled estimates are better than no-assimilation counterparts. Second, the basin-scale runoff assessment demonstrates that across three snow-dominated basins, the assimilation enabled experiment yields systematic improvements in all goodness-of-fit statistics through mitigating the negative effects brought by the fixed long-term precipitation correction factors. For example, when forced by the bias-corrected precipitation, the assimilation-enabled experiment improves the bias by 69%, the root-mean-squared error by 30%, and the unbiased root-mean-squared error by 18% (relative to the no-assimilation counterpart). Finally, the domain-scale assessment is conducted via evaluating against satellite-based SWE and skin temperature products. Both sets of domain-scale analysis further corroborate the findings in the point-scale evaluations. Overall, this study suggests the benefits of the proposed multi-variate assimilation system in improving the cryospheric-hydrological process within a land surface model for use in HMA.

Yuan Xue↗
NASA NTRS2015

Mapping Biophysical Parameters for Land Surface Modeling over the Continental US Using MODIS and Landsat

In terms of the space cities occupy, urbanization appears as a minor land transformation. However, it permanently modifies land's ecological functions, altering its carbon, energy, and water fluxes. It is therefore necessary to develop a land cover characterization at fine spatial and temporal scales to capture urbanization's effects on surface fluxes. We develop a series of biophysical vegetation parameters such as the fraction of photosynthetically active radiation, leaf area index, vegetation greenness fraction, and roughness length over the continental US using MODIS and Landsat products for 2001. A 13-class land cover map was developed at a climate modeling grid (CMG) merging the 500mMODIS land cover and the 30m impervious surface area from the National Land Cover Database. The landscape subgrid heterogeneity was preserved using fractions of each class from the 500 m and 30 m into the CMG. Biophysical parameters were computed using the 8-day composite Normalized Difference Vegetation Index produced by the North American Carbon Program. In addition to urban impact assessments, this dataset is useful for the computation of surface fluxes in land, vegetation, and urban models and is expected to be widely used in different land cover and land use change applications.

Biophysical↗
NASA NTRS1997

Surface Temperature Assimilation in Land Surface Models

This paper examines the utilization of surface temperature as a variable to be assimilated in offline land surface hydrological models. Comparisons between the model computed and satellite observed surface temperatures have been carried out. The assimilation of surface temperature is carried out twice a day (corresponding to the AM and PM overpass of the NOAA10) over the Red- Arkansas basin in the Southwestern United States (31 deg 50 min N - 36 deg N, 94 deg 30 min W - 104 deg 30 min W) for a period of one year (August 1987 to July 1988). The effect of assimilation is to reduce the difference between the surface soil moisture computed for the precipitation and/or shortwave radiation perturbed case and the unperturbed case compared to no assimilation.

Lakshmi, Venkataraman↗
NASA NTRS1999

Surface Temperature Assimilation in Land Surface Models

This paper examines the utilization of surface temperature as a variable to be assimilated in offline land surface hydrological models. Comparisons between the model computed and satellite observed surface temperatures have been carried out. The assimilation of surface temperature is carried out twice a day (corresponding to the AM and PM overpass of the NOAA10) over the Red-Arkansas basin in the Southwestern United States (31 degs 50 sec N - 36 degrees N, 94 degrees 30 seconds W - 104 degrees 3 seconds W) for a period of one year (August 1987 to July 1988). The effect of assimilation is to reduce the difference between the surface soil moisture computed for the precipitation and/or shortwave radiation perturbed case and the unperturbed case compared to no assimilation.

Lakshmi, Venkataraman↗
NASA NTRS2014

Using a Support Vector Machine and a Land Surface Model to Estimate Large-Scale Passive Microwave Temperatures over Snow-Covered Land in North America

A support vector machine (SVM), a machine learning technique developed from statistical learning theory, is employed for the purpose of estimating passive microwave (PMW) brightness temperatures over snow-covered land in North America as observed by the Advanced Microwave Scanning Radiometer (AMSR-E) satellite sensor. The capability of the trained SVM is compared relative to the artificial neural network (ANN) estimates originally presented in [14]. The results suggest the SVM outperforms the ANN at 10.65 GHz, 18.7 GHz, and 36.5 GHz for both vertically and horizontally-polarized PMW radiation. When compared against daily AMSR-E measurements not used during the training procedure and subsequently averaged across the North American domain over the 9-year study period, the root mean squared error in the SVM output is 8 K or less while the anomaly correlation coefficient is 0.7 or greater. When compared relative to the results from the ANN at any of the six frequency and polarization combinations tested, the root mean squared error was reduced by more than 18 percent while the anomaly correlation coefficient was increased by more than 52 percent. Further, the temporal and spatial variability in the modeled brightness temperatures via the SVM more closely agrees with that found in the original AMSR-E measurements. These findings suggest the SVM is a superior alternative to the ANN for eventual use as a measurement operator within a data assimilation framework.

Modeling↗
NASA NTRS1998

Assimilation of Surface Temperature in Land Surface Models

Hydrological models have been calibrated and validated using catchment streamflows. However, using a point measurement does not guarantee correct spatial distribution of model computed heat fluxes, soil moisture and surface temperatures. With the advent of satellites in the late 70s, surface temperature is being measured two to four times a day from various satellite sensors and different platforms. The purpose of this paper is to demonstrate use of satellite surface temperature in (a) validation of model computed surface temperatures and (b) assimilation of satellite surface temperatures into a hydrological model in order to improve the prediction accuracy of soil moistures and heat fluxes. The assimilation is carried out by comparing the satellite and the model produced surface temperatures and setting the "true"temperature midway between the two values. Based on this "true" surface temperature, the physical relationships of water and energy balance are used to reset the other variables. This is a case of nudging the water and energy balance variables so that they are consistent with each other and the true" surface temperature. The potential of this assimilation scheme is demonstrated in the form of various experiments that highlight the various aspects. This study is carried over the Red-Arkansas basin in the southern United States (a 5 deg X 10 deg area) over a time period of a year (August 1987 - July 1988). The land surface hydrological model is run on an hourly time step. The results show that satellite surface temperature assimilation improves the accuracy of the computed surface soil moisture remarkably.

Lakshmi, Venkataraman↗
NASA NTRS2022

Effect of Assimilating SMAP Soil Moisture on CO2 and CH4 Fluxes through Direct Insertion in a Land Surface Model

Soil moisture impacts the biosphere–atmosphere exchange of CO2 and CH4 and plays an important role in the terrestrial carbon cycle. A better representation of soil moisture would improve coupled carbon–water dynamics in terrestrial ecosystem models and could potentially improve model estimates of large-scale carbon fluxes and climate feedbacks. Here, we investigate using soil moisture observations from the Soil Moisture Active Passive (SMAP) satellite mission to inform simulated carbon fluxes in the global terrestrial ecosystem model LPJ-wsl. Results suggest that the direct insertion of SMAP reduces the bias in simulated soil moisture at in situ measurement sites by 40%, with a greater improvement at temperate sites. A wavelet analysis between the model and measurements from 26 FLUXNET sites suggests that the assimilated run modestly reduces the bias of simulated carbon fluxes for boreal and subtropical sites at 1–2-month time scales. At regional scales, SMAP soil moisture can improve the estimated responses of CO2 and CH4 fluxes to extreme events such as the 2018 European drought and the 2019 rainfall event in the Sudd (Southern Sudan) wetlands. The simulated improvements to land–surface carbon fluxes using the direct insertion of SMAP are shown across a variety of timescales, which suggests the potential of SMAP soil moisture in improving the model representation of carbon–water coupling.

SMAP↗
NASA NTRS2007

Assimilation of GRACE Terrestrial Water Storage Data into a Land Surface Model: Results for the Mississippi River Basin

NASA's GRACE mission has the potential to be extremely valuable for water resources applications and global water cycle research. What makes GRACE unique among Earth Science satellite systems is that it is able to monitor variations in water stored in all forms, from snow and surface water to soil moisture to groundwater in the deepest aquifers. However, the space and time resolutions of GRACE observations are coarse. GRACE typically resolves water storage changes over regions the size of Nebraska on a monthly basis, while city-scale, daily observations would be more useful for water management, agriculture, and weather prediction. High resolution numerical (computer) hydrology models have been developed, which predict the fates of water and energy after they strike the land surface as precipitation and sunlight. These are similar to weather and climate forecast models, which simulate atmospheric processes. We integrated the GRACE observations into a hydrology model using an advanced technique called data assimilation. The results were new estimates of groundwater, soil moisture, and snow variations, which combined the veracity of GRACE with the high resolution of the model. We tested the technique over the Mississippi River basin, but it will be even more valuable in parts of the world which lack reliable data on water availability.

Zaitchik, Benjamin F.↗
NASA NTRS2020

The influence of assimilating leaf area index in a land surface model on global water fluxes and storages

Vegetation plays a fundamental role not only in theenergy and carbon cycles but also in the global water balanceby controlling surface evapotranspiration (ET). Thus, accu-rately estimating vegetation-related variables has the poten-tial to improve our understanding and estimation of the dy-namic interactions between the water, energy, and carbon cy-cles. This study aims to assess the extent to which a land sur-face model (LSM) can be optimized through the assimilationof leaf area index (LAI) observations at the global scale. Twoobserving system simulation experiments (OSSEs) are per-formed to evaluate the efficiency of assimilating LAI into anLSM through an ensemble Kalman filter (EnKF) to estimateLAI, ET, canopy-interception evaporation (CIE), canopy wa-ter storage (CWS), surface soil moisture (SSM), and terres-trial water storage (TWS). Results show that the LAI dataassimilation framework not only effectively reduces errorsin LAI model simulations but also improves all the modeledwater flux and storage variables considered in this study (ET,CIE, CWS, SSM, and TWS), even when the forcing pre-cipitation is strongly positively biased (extremely wet con-ditions). However, it tends to worsen some of the modeledwater-related variables (SSM and TWS) when the forcingprecipitation is affected by a dry bias. This is attributed tothe fact that the amount of water in the LSM is conservative,and the LAI assimilation introduces more vegetation, whichrequires more water than what is available within the soil

Terrestrial Vegetation↗