Soil moisture has long been recognized as one of the critical land surface initial conditions for numerical weather, climate hydrological predictions, particularly for transition zones between dry and humid climates. However, none of the currently existing soil moisture products has been used operationally in these models because of their consistency and reliability issues. A consistent and qualitatively reliable global soil moisture product is thus in desire to make good use of observations from different microwave sensors, such as AMSR-E, WindSat and TMI. This study explores the potential of WindSat data for producing such a product using the single channel algorithm (SCA) for soil moisture retrieval in conjunction with field observations for calibrating the algorithm and for validation. The preliminary results show good agreement between the results from WindSat and NASA AMSR-E product both in terms of spatial pattern
and magnitude. The validation results show that the differences between the retrieved soil moisture from WindSat data and the ground measurements are below 0.05 (vol/vol) in most cases, meaning a great potential of WindSat data for producing a blended product. Further cross calibration between the brightness temperatures from different sensors might be needed for producing such a blended product.
Timely and accurate monitoring of global weather anomalies and drought conditions is essential for assessing global
crop conditions. Soil moisture observations are particularly important for crop yield fluctuations provided by the US
Department of Agriculture (USDA) Production Estimation and Crop Assessment Division (PECAD). The current system
utilized by PECAD estimates soil moisture from a 2-layer water balance model based on precipitation and temperature
data from World Meteorological Organization (WMO) and US Air Force Weather Agency (AFWA). The accuracy of
this system is highly dependent on the data sources used; particularly the accuracy, consistency, and spatial and temporal
coverage of the land and climatic data input into the models. However, many regions of the globe lack observations at
the temporal and spatial resolutions required by PECAD. This study incorporates NASA's soil moisture remote sensing
product provided by the EOS Advanced Microwave Scanning Radiometer (AMSR-E) into the U.S. Department of
Agriculture Crop Assessment and Data Retrieval (CADRE) decision support system. A quasi-global-scale operational
data assimilation system has been designed and implemented to provide CADRE a daily product of integrated AMSR-E
soil moisture observations with the PECAD two-layer soil moisture model forecasts. A methodology of the system
design and a brief evaluation of the system performance over the Conterminous United States (CONUS) is presented.
Based on the brightness temperature observations of AMSR-E in 2005 and 2006, this study attempts to find the
simplest and best drought index for identifying drought areas. Totally 8 candidate drought indices were tested from
the AMSR-E brightness temperature data. Assuming that the NASA AMSR-E soil moisture data obtained from
complicated soil moisture retrieval algorithms are the best currently available soil drought indicator, the computed
index with the best correlation with these soil moisture data should the best drought index. For each of the selected
three stations in Hebei province, the temporal correlations between this drought index and the corresponding
precipitation anomalies were computed for the two years. The spatial variations of this drought index and the
precipitation anomaly were also compared with each other for Hebei province in March 2006. Based on these
comparisons, the selected drought index was found to be useful for monitoring drought. Problems for using satellite
microwave observations and future research needs on microwave drought monitoring are discussed.
This study aims to analyze impacts of the NESDIS new product of green vegetation fraction (GVF) data on simulated
surface air temperature and surface fluxes over the continental United States (CONUS) using the Nonhydrostatic
Mesoscale Model (NMM) core of the Weather Research and Forecasting (WRF) system, i.e. WRF-NMM, coupled with
the Noah land surface model (LSM). The new global 0.144 by 0.144 degree GVF dataset is an AVHHR-based, near real-time
weekly dataset starting from 1982. It has better quality and a higher temporal resolution than the old monthly GVF
dataset that is currently used in the NOAA operational numerical weather prediction models. The new weekly
climatology GVF data shows a higher percentage of greenness fraction over most US areas than the old dataset, with the
largest differences by 20-40% over the southeast U.S., the northern Middle West, and the west coast of California in
summer. We have performed some case studies over CONUS during July 2006. In general, using the new GVF data
cools predicted surface temperature over most regions compared to the old data, with the largest cooling over regions
with the largest GVF increase. The latent heat increases significantly over most areas while the sensible heat decreases
slightly. These results are physically consistent as more of the net radiation is dissipated in form of latent heat via
enhanced evapotranspiration in response to increasing vegetation cover. Compared with observations, the new GVF
application reduces the WRF-NMM 2-m surface air temperature warm biases, 2-m relative humidity negative biases, and
their RMSEs.
This study presented the temporal and spatial variation patterns of the seasonal NPP, temperature and precipitation. The NPP simulated by using the GLO-PEM. A semi-mechanistic model of plant photosynthesis and respiration driven entirely by the satellite observations was combined with climate data in Xinjiang of China over the past 20 years to study the impact of seasonal climate changes on the seasonal NPP. The higher correlation coefficients between the seasonal NPP and the corresponding seasonal temperature and precipitation over the past 20 years happened in the areas covered with forest lands,
grasslands, oasis and croplands in the northern and southern foothills of Tianshan Mountain, Iili River Valley, Tarim Basin
and Junggar Basin. In these areas, the vegetation growth was greatly influenced by interannual changes of seasonal temperature and precipitation. The spatial patterns of the correlation coefficients in Xinjiang showed that the higher correlation coefficients between seasonal NPP and seasonal temperature and precipitation in 1990s than in 1980s. With the increased temperature and precipitation, the areas of grasslands and oasis in Xinjiang were expanded over the last 20 years.
In the last several decades, the responses of vegetation to global changes at regional and global scales have been studied with many mathematical models primarily driven by point meteorological observations. In this study, the net primary productivity (NPP) of Xinjiang, China is simulated using the GLObal Production Efficiency Model (GLO-PEM) which is a semi-mechanistic model of plant photosynthesis and respiration and driven entirely by satellite observations. With the available satellite observation data acquired from NOAA’s Advanced Very High Resolution Radiometer (AVHRR), the seasonal and inter-annual changes of NPP in the Xinjiang area are analyzed for the time period of 20 years from 1981 to 2000. Large spatial variability of NPP is found in this area. The temporal trends of NPP in different regions of the area differed significantly. However, for the whole area the mean annual NPP decreased in the 1980s and increased in the 1990s. Seasonal variations of NPP are large and inter-annual changes are moderate. The correlations between the simulated NPP and the precipitation and temperature suggested that precipitation and temperature played major roles in the variations of NPP.
With the large volume of satellite remote sensing data of the earth terrestrial surface becoming available, precisely monitoring the dynamics of the land surface state variables for agricultural and land use management becomes possible. Currently, the moderate resolution imaging spectroradiometers on board NASA’s Earth Observing Satellites (EOS) Terra and Aqua make it possible to derive a global coverage of land surface vegetation indices, leaf area index, and surface temperature data products at 1 km spatial resolution every day. The advanced microwave scanning radiometers (AMSR) on board Aqua and Japan's ADEOS satellites start sending back a global coverage of rainfall and land surface soil moisture data products at up to 25km spatial resolution every two to three days. It is also well known that these land surface remote sensing products contain uncertainties due to imperfect instrumentation calibration and inversion algorithms, geophysical noise, representativeness error, communication breakdowns, and other sources while land surface model can continuously simulate these land surface state or storage variables for all time steps and all covered areas. Therefore a combination of satellite remote sensing products and land surface model simulations may provide more continuous, precise and comprehensive depiction of the dynamics of the land surface states. This paper introduces the state-of-the-arts technologies in the development of NASA's Land Data Assimilation System, and then proposes a procedure to combine the simulations of a simple land surface model and the remote sensing products from MODIS and AMSR. After the results of testing the procedure for an arid area in Southwest USA are presented, the application of the procedure for the oases in Fukang Count of Xinjiang Autonomous Region is proposed.
KEYWORDS: Floods, Data modeling, Decision support systems, Geographic information systems, Remote sensing, 3D modeling, Computing systems, Databases, Telecommunications, Instrument modeling
Decision support system (DSS) is a flexible information technology system that is useful in making semi-structure and non-structure decisions. This paper takes Toutun river basin in Xinjiang as a typical study region, combines "3S" (RS, GIS, GPS), digital 3D virtual emulation and seamless integration of multi-source spatial data with hydrological basin model, forecasting model, reservoir regulating model and damage estimation model to crete a DSS for flood prevention. With this DSS, some difficult issues concerning flood prevention are explored. The characteristics of the DSS for flood prevention for this river basin include: decisions are made spatially-dstributed, real-time, mutual and by group. The DSS is a software platform with diversity and expandability. It contains intelligent and visualization functions.
Soil samples collected from vegetation zone of Fukang oasis in South Junggar Basin were analyzed. The soil samples were air-dried and their PH values, electrical conductivity and total salts were analyzed in the laboratory. Soil samples were weighted and mixed with distilled water by a ratio of 1:5 after stirred and settled over night. Moisture content of soil was measured by the dry-method, PH value was measured by SM-10 digital acidity-meter, and electrical conductivity was measured by weight-method. The PH value, electrical conductivity and total salts of soil were measured from 0-10cm, 10-30cm and 30-50cm vertically. The study uses SAS software to analyze the statistical characteristic of the moisture content, electrical conductivity and total salts of the soil samples. The results show that soil properties are inhomogeneous. From the surface to below 50cm, moisture content and electrical conductivity increase successively, the change of PH value is not significant. The soil is alkalescence. In most circumstances, the data of moisture content, PH value and electrical conductivity are normally distributed. But, in the procession, soil properties influenced by systematic variance of soil properties deviated for normal distribution with various degrees. Soil moisture content, PH value and electrical conductivity do not satisfy simple linear relation at the vegetation zone of Fukang oasis. Using the method of trend surface analysis, the polynomial relation of variances (P<0.01) was obtained and found that moisture content is higher related with electrical conductivity at different depths, but the correlation between moisture content and PH value, and the correlation between PH value and electrical conductivity change with different environment factors.
Terrestrial ecosystems, in which carbon is retained in live biomass, play an important role in the global carbon cycling. Among these ecological systems, vegetation and soils in deserts and semi deserts control significant proportions in the total carbon stocks on the land surface and the carbon fluxes between the land surface and the atmosphere (IPCC special report: Land Use, Land Use Change and Forestry, June 2000). Therefore, accurate assessment of the carbon stocks and fluxes of the desert and semi desert areas at regional scales is required in global carbon cycle studies. In addition,
vegetative ecosystem in semi-arid and arid land is strongly dependent on the water resources. Monitoring the hydrologic processes of the land is thus also required. This work explores the methodology for the sequential continuous estimation of the carbon stocks, CO2 flux, evapotranspiration, and sensible heat fluxes over desert and semidesert area using data from the Jornada desert in New Mexico, USA. A CO2 and energy flux coupled model is used to estimate CO2, water vapor and sensible heat fluxes over the desert area. The model is driven by the observed meteorological data. Its input land surface parameters are derived from satellite images. Simulated energy fluxes are validated for specific sites with eddy covariance observations. Based on
the output of spatially distributed CO2 fluxes, carbon accumulations over the desert area during a period of time is
calculated and the contribution of the desert ecosystem to the atmospheric carbon pool is discussed.
A major problem in operational land cover change detection using remotely sensed data is to separate the change signals caused by land cover changes from those due to vegetation phenology. This study provides an approach to this problem by systematically analyzing the spectral properties of major land cover change processes and the phenological profiles of different land cover types. The phenological profiles were derived from a global data set consisting of a full phenological year's data record of the 1 km monthly composites from the Advanced Very High Resolution Radiometer (AVHRR), while land cover change signals were simulated from the spectral signatures of corresponding land cover types in different seasons. A decision tree method was used to derive the decision rules that provide best separation between the change signals of land cover changes and vegetation phenology. These decision rules were referred to as land cover change trajectories. A complete set of change trajectories was developed for the globe in all seasons of a phenological year. Results from this study indicate that during most seasons of a phenological year, major land cover change processes including deforestation, denudation, revegetation, flooding, flood receding and vegetation burning, can be separated form one another and from vegetation phenology in the red-near infrared space. The derived trajectories, when validated, can serve as a theoretical basis for developing operational change detection algorithms.
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