It is very necessary to validate MODIS land surface temperature (LST) for its application, especially in the arid and
semi-arid regions. In this study, the Terra and Aqua MODIS 1km daily LST products (MOD/MYD11A1) are validated
using ground based longwave radiation observation. The longwave radiation ground measurements during 2008 to 2009
were collected from four automatic weather stations in the Heihe River Basin. In this validation process, the land surface
broadband emissivities of the validation stations were obtained from ASTER Spectral library. Then the ground-measured
LSTs of validation stations were converted from surface longwave radiation based on Stefan-Boltzmann's law and
thermal radioactive transfer theory. The validation results indicated that: except for DYKGT station, the mean bias was
less then 1K and the mean absolute error (MAE) range was about 2-3K; MYD11A1 LSTs from Aqua have larger biases,
MAEs, and RMSDs than that of MOD11A1 LSTs from Terra in most cases. The comparisons with ground measured LSTs show that the MAEs and RMSDs from daytime MOD/MYD11A1 comparisons are larger than that from nighttime MOD/MYD11A1 comparisons.
Gross Primary Production (GPP) is the sum of carbon absorbed by plant canopy. It is a key measurement of carbon mass
flux in carbon cycle studies. Remote sensing based light use efficiency model is a widely used method to estimate
regional GPP. In this study, MODIS-PSN was used to estimate GPP in Heihe River Basin. In order to better the model
accuracy, maximum light use efficiency (ε0) in MODIS-PSN is estimated using local observed carbon flux data and
meteorological data. After adjustment of parameter ε0, MODIS-PSN can correctly estimate GPP for major vegetation
type in the Heihe River Basin. Then, yearly GPP over Heihe River Basin was estimated. The results indicated that about
1.4*1013g carbon enter terrestrial ecosystem through vegetation photosynthesis in the Heihe River Basin one year. In
contrast, there is just 5.73*1013g carbon enter terrestrial ecosystem according to the standard MODIS GPP product,
which is greatly underestimated GPP in the Heihe River Bain.
KEYWORDS: Vegetation, Climatology, Meteorology, Temperature metrology, Remote sensing, Environmental sensing, Climate change, Data modeling, Data conversion, Analytical research
Based on the protensive GIMMS NDVI data set and meteorological data during 1982-2009 in the Heihe River Basin, a
novel multiple time-scale analysis method, Empirical Mode Decomposition (EMD), is used to diagnose the periodicities
of NDVI, air temperature and precipitation data. At the same time, the relationship among these three elements is
performed. The results indicate that SINDVI, temperature and precipitation have the similar 3 and 10 years quasiperiodic
in the upper reaches of the Heihe River Basin. SINDVI and temperature have the similar 3 and 10 years quasiperiodic,
SINDVI and precipitation have the similar 3, 6, 8 and 15 years quasi-periodic in the middle reaches of the
Heihe River Basin. In the meantime, in the lower reaches of the Heihe River, SINDVI and temperature have the similar 3
and 10 years quasi-periodic, SINDVI and precipitation have the similar 3 and 6 years quasi-periodic. It is indicated that
the temperature and precipitation are both the driving factor affecting the vegetation in the Heihe River Basin. In
addition, the EMD method can be effectively used to analyze the relationship between time series data and the
meteorological data.
This research dealt with a daytime integration method with the help of Simple Biosphere Model, Version 2 (SiB2).
The field observations employed in this study were obtained at the Yingke (YK) oasis super-station, which includes an
Automatic Meteorological Station (AMS), an eddy covariance (EC) system and a Soil Moisture and Temperature
Measuring System (SMTMS). This station is located in the Heihe River Basin, the second largest inland river basin in
China. The remotely sensed data and field observations employed in this study were derived from Watershed Allied
Telemetry Experimental Research (WATER). Daily variations of EF in temporal and spatial scale would be detected by
using SiB2. An instantaneous midday EF was calculated based on a remote-sensing-based estimation of surface energy
budget. The invariance of daytime EF was examined using the instantaneous midday EF calculated from a
remote-sensing-based estimation. The integration was carried out using the constant EF method in the intervals with a
steady EF. Intervals with an inconsistent EF were picked up and ET in these intervals was integrated separately. The truth
validation of land Surface ET at satellite pixel scale was carried out using the measurement of eddy covariance (EC) system.
Process-oriented dynamic vegetation models are effective tools to assess carbon and water exchanges between vegetation
and environment for different scales. Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM) is one of the
well-established, process-oriented dynamic vegetation models. It can simulate seasonal trends of EvapoTranspiration
(ET) and Net Ecosystem Exchange (NEE) forced by weather data. In this study, LPJ-DGVM was employed to simulate
the ET and NEE in Yingke (YK) oasis station and A'Rou (AR) freeze/thaw observation station. The results indicate that
LPJ-DGVM could not make good estimations in both YK station and AR station. The simulation results were validated
with the water and CO2 flux observation from Eddy Covariance (EC). The freeze-thaw phenomenon and irrigation have
great impacts on soil water content dynamic in arid region, but they are not considered in LPJ-DGVM. In order to
improve the simulation accuracy, a soil water content data assimilation scheme was designed. The observed soil water
content was assimilated into LPJ-DGVM with Ensemble Kalman Filter (EnKF) algorithm. The simulation accuracy of
LPJ-DGVM was improved obviously when soil water content was assimilated into LPJ-DGVM. The EnKF is effective
for assimilating in situ observation.
The northwest China, typical arid and semi-arid regions, is the first or second-degree sensitivity zones for global change. Monitoring vegetation change is an important method to study the impacts of global climate change. Time-series satellite remote sensing data make it possible to monitor vegetation at different spatial and temporal resolutions globally. A long time series of Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) data with 8x8 km2 spatial resolution and during 1982 to 2003 were used to monitor the vegetation cover in the northwest China. The monitoring results indicate an obvious greening trend exists. The precipitation and relative humidity have high correlations with the SINDVI. So the water condition is the most important factors for the spatial distribution of the SINDVI levels. The precipitation and temperature are the primary driving factors for inter-annual vegetation changes.
Amorphous multilayer thin film a-C:H/a-Si:H was deposited by magnetron sputtering. X-ray diffraction and Auger electron spectroscopy measurements indicate very well the periodicity of the sample. The shift and broadening of the photoluminescence peak are interpreted in light of quantum size effect.
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