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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Daily MODIS 500 m Reflectance Anisotropy Direct Broadcast (DB) Products for Monitoring Vegetation Phenology Dynamics

Land surface vegetation phenology is an efficient bio-indicator for monitoring ecosystem variation in response to changes in climatic factors. The primary objective of the current article is to examine the utility of the daily MODIS 500 m reflectance anisotropy direct broadcast (DB) product for monitoring the evolution of vegetation phenological trends over selected crop, orchard, and forest regions. Although numerous model-fitted satellite data have been widely used to assess the spatio-temporal distribution of land surface phenological patterns to understand phenological process and phenomena, current efforts to investigate the details of phenological trends, especially for natural phenological variations that occur on short time scales, are less well served by remote sensing challenges and lack of anisotropy correction in satellite data sources. The daily MODIS 500 m reflectance anisotropy product is employed to retrieve daily vegetation indices (VI) of a 1 year period for an almond orchard in California and for a winter wheat field in northeast China, as well as a 2 year period for a deciduous forest region in New Hampshire, USA. Compared with the ground records from these regions, the VI trajectories derived from the cloud-free and atmospherically corrected MODIS Nadir BRDF (bidirectional reflectance distribution function) adjusted reflectance (NBAR) capture not only the detailed footprint and principal attributes of the phenological events (such as flowering and blooming) but also the substantial inter-annual variability. This study demonstrates the utility of the daily 500 m MODIS reflectance anisotropy DB product to provide daily VI for monitoring and detecting changes of the natural vegetation phenology as exemplified by study regions comprising winter wheat, almond trees, and deciduous forest.

Vegetation↗
NASA NTRS2015

Investigation of North American Vegetation Variability under Recent Climate: A Study Using the SSiB4/TRIFFID Biophysical/Dynamic Vegetation Model

Recent studies have shown that current dynamic vegetation models have serious weaknesses in reproducing the observed vegetation dynamics and contribute to bias in climate simulations. This study intends to identify the major factors that underlie the connections between vegetation dynamics and climate variability and investigates vegetation spatial distribution and temporal variability at seasonal to decadal scales over North America (NA) to assess a 2-D biophysical model/dynamic vegetation model's (Simplified Simple Biosphere Model version 4, coupled with the Top-down Representation of Interactive Foliage and Flora Including Dynamics Model (SSiB4/TRIFFID)) ability to simulate these characteristics for the past 60 years (1948 through 2008). Satellite data are employed as constraints for the study and to compare the relationships between vegetation and climate from the observational and the simulation data sets. Trends in NA vegetation over this period are examined. The optimum temperature for photosynthesis, leaf drop threshold temperatures, and competition coefficients in the Lotka-Volterra equation, which describes the population dynamics of species competing for some common resource, have been identified as having major impacts on vegetation spatial distribution and obtaining proper initial vegetation conditions in SSiB4/TRIFFID. The finding that vegetation competition coefficients significantly affect vegetation distribution suggests the importance of including biotic effects in dynamical vegetation modeling. The improved SSiB4/TRIFFID can reproduce the main features of the NA distributions of dominant vegetation types, the vegetation fraction, and leaf area index (LAI), including its seasonal, interannual, and decadal variabilities. The simulated NA LAI also shows a general increasing trend after the 1970s in responding to warming. Both simulation and satellite observations reveal that LAI increased substantially in the southeastern U.S. starting from the 1980s. The effects of the severe drought during 1987-1992 and the last decade in the southwestern U.S. on vegetation are also evident from decreases in the simulated and satellite-derived LAIs. Both simulated and satellite-derived LAIs have the strongest correlations with air temperature at northern middle to high latitudes in spring reflecting the effect of these climatic variables on photosynthesis and phenological processes. Meanwhile, in southwestern dry lands, negative correlations appear due to the heat and moisture stress there during the summer. Furthermore, there are also positive correlations between soil wetness and LAI, which increases from spring to summer. The present study shows both the current improvements and remaining weaknesses in dynamical vegetation models. It also highlights large continental-scale variations that have occurred in NA vegetation over the past six decades and their potential relations to climate. With more observational data availability, more studies with differentmodels and focusing on different regions will be possible and are necessary to achieve comprehensive understanding of the vegetation dynamics and climate interactions.

climate variability↗
NASA NTRS2012

Hydroclimatic Controls over Global Variations in Phenology and Carbon Flux

The connection between phenological and hydroclimatological variations are quantified through joint analyses of global NDVI, LAI, and precipitation datasets. The global distributions of both NDVI and LAI in the warm season are strongly controlled by three quantities: mean annual precipitation, the standard deviation of annual precipitation, and Budyko's index of dryness. Upon demonstrating that these same basic (if biased) relationships are produced by a dynamic vegetation model (the dynamic vegetation and carbon storage components of the NCAR Community Land Model version 4 combined with the water and energy balance framework of the Catchment Land Surface Model of the NASA Global Modeling and Assimilation Office), we use the model to perform a sensitivity study focusing on how phenology and carbon flux might respond to climatic change. The offline (decoupled from the atmosphere) simulations show us, for example, where on the globe a given small increment in precipitation mean or variability would have the greatest impact on carbon uptake. The analysis framework allows us in addition to quantify the degree to which climatic biases in a free-running GCM are manifested as biases in simulated phenology.

Koster, Randal↗
NASA NTRS2021

Drought Variability over the Conterminous United States for the past century

We examine the drought variability over the Conterminous United States (CONUS) for 1915-2018 using the Noah-MP land-surface model. We examine different model options on drought reconstruction including optional representation of groundwater and dynamic vegetation phenology. Over our 104-year reconstruction period, we identify 12 great droughts that each covered at least 36% of CONUS and lasted for at least 5 months. The great droughts tend to have smaller areas when groundwater and/or dynamic vegetation are included in the model configuration. We detect a small decreasing trend in dry area coverage over CONUS in all configurations. We identify 45 major droughts in the baseline (with a dry area coverage greater than 23.6% of CONUS) that are, on average, somewhat less severe than great droughts. We find that representation of groundwater tends to increase drought duration for both great and major droughts, primarily by leading to earlier drought onset (some due to short-lived recovery from a previous drought) or later demise (groundwater anomalies lag precipitation anomalies). In contrast, representation of dynamic vegetation tends to shorten major droughts duration, primarily due to earlier drought demise ( closed stoma or dead vegetation reduces ET loss during droughts). On a regional basis, the U.S. Southwest (Southeast) has the longest (shortest) major drought durations. Consistent with earlier work, dry area coverage in all subregions except the Southwest has decreased. The effects of groundwater and dynamic vegetation vary regionally due to differences in groundwater depths (hence connectivity with the surface) and vegetation types.

drought↗
NASA NTRS2020

Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions

Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we applied a modified Soil and Water Assessment Tool (SWAT) to quantify the extent of improvements that the assimilation of remotely sensed Leaf Area Index (LAI) would convey to streamflow, soil moisture, and nitrate load simulations across a 16,860 km2 agricultural watershedin the midwestern United States. We modified the SWAT source code to automatically override the model’s built-in semiempirical LAI with spatially distributed and temporally continuous estimates from Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to a “basic” traditional model with limited spatial information, our LAI assimilation model (i) significantly improved daily streamflow simulations during medium-to-low flow conditions, (ii) provided realistic spatial distributions of growing season soil moisture, and (iii) substantially reproduced the long-term observed variability of daily nitrate loads. Further analysis revealed that the overestimation or underestimation of LAI imparted a proportional cascading effect on how the model partitions hydrologic fluxes and nutrient pools. As such, assimilation of MODIS LAI data corrected the model’sLAI overestimation tendency, which led to a proportionally increased rootzone soil moisture and decreased plant nitrogen uptake. With these new findings, our study fills the existing knowledge gap regarding vegetation dynamics in watershed modeling and confirms that assimilation of MODIS LAI data in watershed models can effectively improve both hydrology and water quality predictions.

Adnan Rajib↗
NASA NTRS1994

Radiative transfer in shrub savanna sites in Niger: Preliminary results from HAPEX-Sahel. Part 3: Optical dynamics and vegetation index sensitivity to biomass and plant cover

A shrub savannah landscape in Niger was optically characterized utilizing blue, green, red and near-infrared wavelengths. Selected vegetation indices were evaluated for their performance and sensitivity to describe the complex Sahelian soil/vegetation canopies. Bidirectional reflectance factors (BRF) of plants and soils were measured at several view angles, and used as input to various vegetation indices. Both soil and vegetation targets had strong anisotropic reflectance properties, rendering all vegetation index (6) responses to be a direct function of sun and view geometry. Soil background influences were shown to alter the response of most vegetation indices. N-space greenness had the smallest dynamic range in VI response, but the n-space brightness index provided additional useful information. The global environmental monitoring index (GEMI) showed a large 6 dynamic range for bare soils, which was undesirable for a vegetation index. The view angle response of the normalized difference vegetation index (NDVI), atmosphere resistant vegetation index (ARVI) and soil atmosphere resistant vegetation index (SARVI) were asymmetric about nadir for multiple view angles, and were, except for the SARVI, altered seriously by soil moisture and/or soil brightness effects. The soil adjusted vegetation index (SAVI) was least affected by surface soil moisture and was symmetric about nadir for grass vegetation covers. Overall the SAVI, SARVI and the n-space vegetation index performed best under all adverse conditions and were recommended to monitor vegetation growth in the sparsely vegetated Sahelian zone.

vanLeeuwen, W. J. D.↗
NASA NTRS1992

Comparison of multi-temporal NOAA-AVHRR and SPOT-XS satellite data for mapping land-cover dynamics in the West African Sahel

Multi-resolution and multi-temporal remote sensing data (SPOT-XS and AVHRR) were evaluated for mapping local land-cover dynamics in the Sahel of West Africa. The aim of this research was to evaluate the agricultural information that could be derived from both high and low spatial resolution data in areas where there is very often limited ground information. A combination of raster-based image processing and vector-based geographical information system mapping was found to be effective for understanding both spatial and spectral land-cover dynamics. The SPOT data proved useful for mapping local land-cover classes in a dominantly recessive agricultural region. The AVHRR-LAC data could be used to map the dynamics of riparian vegetation, but not the changes associated with recession agriculture. In areas where there was a complex mixture of recession and irrigated agriculture, as well as riparian vegetation, the AVHRR data did not provide an accurate temporal assessment of vegetation dynamics.

Marsh, S. E.↗
NASA NTRS2022

Satellite Soil Moisture Data Assimilation Impacts on Modeling Weather Variables and Ozone in the Southeastern US – Part 2: Sensitivity to Dry-Deposition Parameterizations

Ozone (O3) dry deposition is a major O3 sink. As a follow-up study of Huang et al. (2021), we quantify the impact of satellite soil moisture (SM) on model representations of this process when different dry-deposition parameterizations are implemented, based on which the implications for interpreting O3 air pollution levels and assessing the O3 impacts on human and ecosystem health are provided. The SM data from NASA's Soil Moisture Active Passive mission are assimilated into the Noah-Multiparameterization (Noah-MP) land surface model within the NASA Land Information System framework, semicoupled with Weather Research and Forecasting model with online Chemistry (WRF-Chem) regional-scale simulations covering the southeastern US. Major changes in the modeling system used include enabling the dynamic vegetation option, adding the irrigation process, and updating the scheme for the surface exchange coefficient. Two dry-deposition schemes are implemented, i.e., the Wesely scheme and a “dynamic” scheme, in the latter of which dry-deposition parameterization is coupled with photosynthesis and vegetation dynamics. It is demonstrated that, when the dynamic scheme is applied, the simulated O3 dry-deposition velocities vd and their stomatal and cuticular portions, as well as the total O3 fluxes Ft, are larger overall; vd and Ft are 2–3 times more sensitive to the SM changes due to the data assimilation (DA). Further, through case studies at two forested sites with different soil types and hydrological regimes, we highlight that, applying the Community Land Model type of SM factor controlling stomatal resistance (i.e., β factor) scheme in replacement of the Noah-type β factor scheme reduced the v(d) sensitivity to SM changes by ∼75 % at one site, while it doubled this sensitivity at the other site. Referring to multiple evaluation datasets, which may be associated with variable extents of uncertainty, the model performance of vegetation, surface fluxes, weather, and surface O3 concentrations shows mixed responses to the DA, some of which display land cover dependency. Finally, using model-derived concentration- and flux-based policy-relevant O3 metrics as well as their matching exposure–response functions, the relative biomass/crop yield losses for several types of vegetation/crops are estimated to be within a wide range of 1 %–17 %. Their sensitivities to the model's dry-deposition scheme and the implementation of SM DA are discussed.

Min Huang↗
NASA NTRS2018

The SMAP Level-4 ECO Project: Improving Terrestrial Flux Estimates Through Coupled Hydrology-Vegetation Data Assimilation

Simulations of hydrologic and vegetation states as well as water, energy and carbon fluxes from the land surface to the atmosphere are crucial for a wide range of applications, including agricultural advisories, forecasts of (short-term) atmospheric behavior and seasonal weather predictions including forecasts of extreme events, such as heatwaves or droughts. The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Eco-Hydrology (L4-ECO) project aims to improve modeled estimates of the terrestrial water, energy and carbon fluxes and states by developing a fully-coupled hydrology-vegetation data assimilation system. This system is developed around the NASA Goddard Earth Observing System (GEOS) Catchment-CN land surface model, which combines land hydrology and energy balance components of the GEOS Catchment model with dynamic vegetation components of the Community Land Model version 4. Catchment-CN fully couples the terrestrial water, energy and carbon cycles, allowing feedbacks from the land hydrology to the biosphere and vice versa.Here, we implement a calibration of the Catchment-CN vegetation parameterization against observations of the fraction of absorbed photosynthetically active radiation (FPAR) from the Moderate Resolution Imaging Spectroradiometer (MODIS) to improve the model's standalone skill. Later, the DA algorithm used to produce the SMAP L4 soil moisture product will be adapted to Catchment-CN to assimilate SMAP brightness temperatures and inform the model's land hydrology component. Finally, the DA system will be further extended to assimilate MODIS FPAR observations in order to constrain the model's dynamic vegetation component.In this presentation, we demonstrate that the Catchment-CN parameter calibration leads to more realistic vegetation simulations and reduces the root mean squared error between modeled and observed vegetation states across the model's various plant functional types. We also show that the assimilation of SMAP observations is able to improve the average correlation, bias and unbiased RMSE between the modeled surface and root zone soil moisture estimates, and ground observations from the SMAP core validation sites.

Kolassa, Jana↗
NASA NTRS2018

The SMAP Level-4 ECO Product - Phase 1: Improving Vegetation Simulations Through Observation-Driven Parameter Estimation

Simulations of hydrological fields as well as water, energy and carbon fluxes from the land surface to the atmosphere are crucial for a wide range of applications, including agricultural advisories, forecasts of (short-term) atmospheric behavior and seasonal weather predictions including forecasts of extreme events, such as heatwaves or droughts. The NASA Soil Moisture Active Passive (SMAP) mission Level-4 (L4) Eco-Hydrology (ECO) project aims to improve modeled estimates of the terrestrial water, energy and carbon fluxes and states by developing a fully-coupled hydrology-vegetation data assimilation (DA) algorithm. The DA system is developed for the NASA Goddard Earth Observing System version 5 (GEOS-5) Catchment-CN land surface model, which combines land hydrology components of the GEOS-5 Catchment model with dynamic vegetation components of the Community Land Model version 4. Catchment-CN fully couples the terrestrial water, energy and carbon cycles, allowing feedbacks from the land hydrology to the biosphere and vice versa. For SMAP L4 ECO a calibration of the Catchment-CN vegetation parameterization against observations of the fraction of absorbed photosynthetically active radiation (FPAR) from the Moderate Resolution Imaging Spectroradiometer (MODIS) is implemented to improve the model's standalone skill. Next, the DA algorithm used to produce the SMAP L4 soil moisture product is adapted to Catchment-CN to assimilate SMAP brightness temperatures and inform the model's land hydrology component. The DA system is further extended to assimilate MODIS FPAR observations in order to constrain the model's dynamic vegetation component. In this presentation, we demonstrate that the Catchment-CN parameter calibration leads to more realistic vegetation simulations and reduces the root mean squared error between modeled and observed vegetation states across the model's various plant functional types. We also show that the assimilation of SMAP observations is able to improve the average correlation, bias and unbiased RMSE between the modeled surface and root zone soil moisture estimates, and ground observations from the SMAP core validation sites.

Kolassa, J.↗
NASA NTRS2021

Assimilation of vegetation conditions improves the representation of drought over agricultural areas

This study presents an evaluation of the impact of vegetation conditions on a land-surface model (LSM) simulation of agricultural drought. The Noah-MP LSM is used to simulate water and energy fluxes and states, which are transformed into drought categories using percentiles over the continental U.S. from 1979 to 2017. Leaf Area Index (LAI) observations are assimilated into the dynamic vegetation scheme of Noah-MP. A weekly operational drought monitor (the U.S. Drought Monitor) is used for the evaluation. The results show that LAI assimilation into Noah-MP’s dynamic vegetation scheme improves the model’s ability to represent drought, particularly over cropland areas. LAI assimilation improves the simulation of the drought category, detection of drought conditions, and reduces the instances of drought false alarms. The assimilation of LAI in these locations not only corrects model errors in the simulation of vegetation, but also can help to represent unmodeled physical processes such as irrigation towards improved simulation of agricultural drought.

David M Mocko↗
NASA NTRS2005

Carbon Dynamics in Vegetation and Soils

The overall goals of CD-08 team in Phase I were to quantify the contributions of different components of the carbon cycle to overall ecosystem carbon balance in Amazonian tropical forests and to undertake process studies at a number of sites along the eastern LBA transect to understand how and why these fluxes vary with site, season, and year. We divided this work into a number of specific tasks: (1) determining the average rate (and variability) of tree growth over the past 3 decades; (2) determining age demographics of tree populations, using radiocarbon to determine tree age; (3) assessing the rate of production and decomposition of dead wood debris; (4) determining turnover rates for organic matter in soils and the mean age of C respired from soil using radiocarbon measurements; and (5) comparing our results with models and constructing models to predict the potential of tropical forests to function as sources or sinks of C. This report summarizes the considerable progress made towards our original goals, which have led to increased understanding of the potential for central Amazon forests to act as sources or sinks of carbon with altered productivity. The overall picture of tropical forest C dynamics emerging from our Phase I studies suggests that the fraction of gross primary production allocated to growth in these forests is only 25-30%, as opposed to the 50% assumed by many ecosystem models. Consequent slow tree growth rates mean greater mean tree age for a given diameter, as reflected in our measurements and models of tree age. Radiocarbon measurements in leaf and root litter suggest that carbon stays in living tree biomass for several years up to a decade before being added to soils, where decomposition is rapid. The time lags predicted from 14C, when coupled with climate variation on similar time scales, can lead to significant interannual variation in net ecosystem C exchange.

Trumbore, Susan↗
NASA NTRS1991

Dynamics of vegetation and soils of oak/saw palmetto scrub after fire: Observations from permanent transects

Ten permanent 15 m transects previously established in two oak/saw palmetto scrub stands burned in December 1986, while two transects remained unburned. Vegetation in the greater than 0.5 m and the less than 0.5 m layers on these transects was sampled at 6, 12, 18, 24, and 36 months postburn and determined structural features of the vegetation (height, percent bare ground, total cover). The vegetation data were analyzed from each sampling by height layer using detrended correspondence analysis ordination. Vegetation data for the greater than 0.5 m layer for the entire time sequence were combined and analyzed using detrended correspondence analysis ordination. Soils were sampled at 6, 12, 18, and 24 months postburn and analyzed for pH, conductivity, organic matter, exchangeable cations (Ca, Mg, K, Na), NO3-N, NH4-N, Al, available metals (Cu, Fe, Mn, Zn), and PO4-P. Shrub species recovered at different rates postfire with saw palmetto reestablishing cover greater than 0.5 m within one year, but the scrub oaks had not returned to preburn cover greater than 0.5 m in 3 years after the fire. These differences in growth rates resulted in dominance shifts after the fire with saw palmetto increasing relative to the scrub oaks. Overall changes in species richness were minor, although changes occurred in species richness by height layers due to different growth rates. Soils of well drained and poorly drained sites differed markedly. Soil responses to the fire appeared minor. Soil pH increased at 6 and 12 months postfire; calcium increased at 6 months postburn. Nitrate-nitrogen increased at 12 months postburn. Low values of conductivity, PO4-P, Mg, K, Na, and Fe at 12 months postburn may be related to heavy rainfall the preceding month. Seasonal variability in some soil parameters appeared to occur.

Schmalzer, Paul A.↗
NASA NTRS1996

Dynamics of vegetative cytoplasm during generative cell formation and pollen maturation in Arabidopsis thaliana

Ultrastructural changes of pollen cytoplasm during generative cell formation and pollen maturation in Arabidopsis thaliana were studied. The pollen cytoplasm develops a complicated ultrastructure and changes dramatically during these stages. Lipid droplets increase after generative cell formation and their organization and distribution change with the developmental stage. Starch grains in amyloplasts increase in number and size during generative and sperm cell formation and decrease at pollen maturity. The shape and membrane system of mitochondria change only slightly. Dictyosomes become very prominent, and numerous associated vesicles are observed during and after sperm cell formation. Endoplasmic reticulum appears extensively as stacks during sperm cell formation. Free and polyribosomes are abundant in the cytoplasm at all developmental stages although they appear denser at certain stages and in some areas. In mature pollen, all organelles are randomly distributed throughout the vegetative cytoplasm and numerous small particles appear. Organization and distribution of storage substances and appearance of these small particles during generative and sperm cell formation and pollen maturation are discussed.

Non-NASA Center↗