This paper proposes an improved statistical method for fusing carbon dioxide (CO2) data retrieved from two major instruments, the Greenhouse gases Observing SATellite (GOSAT) and the Atmospheric Infrared Sounder (AIRS). These two datasets were fused to obtain CO2 concentrations near the surface, which is a region that is especially important for studies on carbon sources and sinks. Overall, the CO2 monthly average values from GOSAT are all lower than those from AIRS from 2010 to 2012. The datasets show the similar seasonal cycles of carbon dioxide and show an increasing trend with a determination coefficient of 0.45. A strong correlation was determined by adding the climatic factors as independent variables for regression analysis. The correlation coefficients between the CO2 values from AIRS and GOSAT significantly increased in response. The true CO2 data processes were then predicted using the fixed rank kriging method. This showed that the data-fusion CO2 product provides more reasonable information and that the corresponding mean squared prediction errors are smaller than those from the single GOSAT CO2 dataset.
Normalized Difference Vegetation Index (NDVI) is one of the most widely used indicators for monitoring the vegetation coverage in land surface. The time series features of NDVI are capable of reflecting dynamic changes of various ecosystems. Calculating NDVI via Moderate Resolution Imaging Spectrometer (MODIS) and other wide-swath remotely sensed images provides an important way to monitor the spatial and temporal characteristics of large-scale NDVI. However, difficulties are still existed for ecologists to extract such information correctly and efficiently because of the problems in several professional processes on the original remote sensing images including radiometric calibration, geometric correction, multiple data composition and curve smoothing. In this study, we developed an efficient and convenient online toolbox for non-remote sensing professionals who want to extract NDVI time series with a friendly graphic user interface. It is based on Java Web and Web GIS technically. Moreover, Struts, Spring and Hibernate frameworks (SSH) are integrated in the system for the purpose of easy maintenance and expansion. Latitude, longitude and time period are the key inputs that users need to provide, and the NDVI time series are calculated automatically.
The aim of this work is to use narrow band normalized difference vegetation indices to compare the estimations of chlorophyll contents at foliar level and canopy level, through a large number of simulated canopy reflectance spectra under different chlorophyll contents based on PROSPECT model and SAIL model. 10 narrow band NDVIs were selected at the identified ranges that can effectively assess foliar chlorophyll content. We analyzed the correlations between canopy chlorophyll contents and the ten narrow band NDVIs firstly, and then analyze these indices’ sensitivities to all canopy parameters, the adaptation of the 10 narrow band NDVIs used in assessing the canopy chlorophyll content were evaluated finally. We found that only two narrow band NDVIs (i.e., NDVI(875, 725) and NDVI(900,720)) can be applied for the estimation of chlorophyll contents at canopy level.
The atmospheric pollution and air quality issues are getting worse in China, the formation mechanism of aerosols and their environment effects attracted more and more attention. Aerosol Optical Depth (AOD) is one of the most important parameters which can indicate the atmospheric turbidity and aerosol load. High-quality AOD data are significant for the study in the atmospheric environment (i.e., air quality). This paper used MODIS/Terra AOD in 2008 to improve the coverage of MODIS/Aqua AOD, which was based on linear regression analysis model. RMSE between estimation value and AquaAOD detected through satellite is 0.132. The average value of test data was 0.812. The average of regression result was 0.807. It showed that the regression model between AODTerra and AODAqua worked well. Also, we built two sets of estimation models (MODIS AOD and OMI AOD) through stepwise regression analysis model. One is using OMI AOD and meteorological elements to estimate MODIS AOD. The value of RMSE was 0.113, which represents 13.916% of the average(R2=0.782). The other one is using MODIS AOD and meteorological elements to estimate OMI AOD. RMSE of the model is 0.132, which represents 18.182% of the average (R2=0.726).
KEYWORDS: Clouds, Data modeling, Atmospheric modeling, Convection, Meteorology, Monte Carlo methods, Data centers, Computer simulations, Systems modeling, Physics
The Weather Research and Forecast Model (WRF) version 3.5 has been used in this study to simulate a heavy rainfall event during the Meiyu season that occurred between 1 and 2 July 2014 over the Yangtze River valley (YRV) in China. The WRF model is driven by the National Centers for Environmental Predictions (NCEP) Final (FNL) global tropospheric analysis data, and eight WRF nested experiments using four different microphysics (MP) schemes and two cumulus parameterizations (CP) are conducted to evaluate the effects of these microphysics and cumulus schemes on heavy rainfall predictions over YRV region. The four MPs selected in this study are Lin et al., WRF Single-Moment 3-class scheme (WSM3), WRF Single-Moment 5-class scheme (WSM5) and WRF Single-Moment 6-class scheme (WSM6), and the two CPs are Kain-Fristch (KF) and Betts-Miller-Janjic (BMJ) schemes. Sensitivity studies showed that all MPs coupling with KF and BMJ CP schemes can well capture the major rain belt from the northeast to southwest with three rainfall centers, but largely overestimate the rainfall near the border between Anhui and Hubei provinces along with the Yellow Sea shore, which produce an opposite trend compared to the observations. Large discrepancies are also presented in WRF simulations of heavy rainfall centers regarding their locations and magnitudes. All MPs coupling with KF CP scheme produced the rainfall areas shifting towards east compared to the observations, while all MPs with BMJ CP scheme tend to better predict the rainfall patterns with slightly more fake precipitation centers. Among all the experiments, the BMJ cumulus scheme has superiority in simulating the Meiyu rainfall over the KF scheme, and the WSM5–BMJ combination shows the best predictive skills.
In this paper, the AOD over Beijing on Sep. 4th and Oct. 6th, 2014 are retrieved by applying DDV method using Landsat8/OLI data. Both cases show that retrieved AOD over Beijing has a higher value over urban areas and a relative lower value over mountainous area, and the distribution of AOD is greatly influenced by the land cover type. The ground-based PM2.5 data is also used to validate the derived AOD, and there is a good agreement between the AOD and PM2.5 concentrations with a high correlation coefficient of 0.8742. The calculated AOD reflects the detailed information of the atmosphere over Beijing due to its higher spatial resolution, and it is concluded that the DDV algorithm can be applied to Landsat8/OLI to retrieve AOD over Beijing.
The aim of this work was to predict responses of reference evapotranspiration (ETo) to perturbations of four climatic variables in Shandong province, China. For this purpose, ETo was estimated based on the FAO-56 Penman–Monteith equation, a non-dimensional relative sensitivity coefficient was employed. Climatic variables (i.e., daily air temperature, sunshine duration, wind speed and daily relative humidity) at 12 meteorological stations covering whole area (1960 to 2013) were collected firstly and used for the analysis. Results showed that ETo had positive sensitivities to air temperature, sunshine duration and wind speed, opposite to what were observed to relative humidity. The sensitivity of climatic parameters to ETo showed a decreasing trend: relative humidity> >sunshine duration>wind speed > air temperature. The sensitivity coefficients of different factors varied in time and space. From 1960 to 2013, the sensitivity coefficient of sunshine duration (Sn) showed a downward trend at a rate of (-4.3e-4)/a. The sensitivity coefficient of wind speed (SWS) and relative humidity (SRH) increased at a rate of (3.9e-4)/a and (1.9e-3)/a respectively, while the sensitivity coefficient of air temperature (ST) waved with a tiny decrease trend. The values of ST and Sn in southern were larger than in northern region. The values of SWS in southern and northeast region were smaller than that in the northern area. SRH in the central region was lower than other area, opposite to what were observed in coastal areas.
With the increasingly prevalent and far-reaching application of remote sensing, several algorithms have been put forward for land surface temperature retrieval. However, there is still no consensus on the calculation of land surface emissivity (LSE), which is one of the significant parameters in land surface temperature (LST) retrieval. In this paper, two methods of estimating LSE based on thematic mapper data were introduced: Van’s empirical formula method and the mixed pixels method. Based on the detailed introduction to Van’s empirical formula and the mixed pixels decomposing method in computing surface emissivity, Landsat-8 thermal infrared data and the radiative transfer equation method were used to obtain the land surface temperature in Taihu region. In this paper, atmospheric parameters are based on real-time atmospheric profile to reduce the LST error brought by the atmospheric profile. Two figures were acquired, which represented the LST of Van’s empirical formula and the mixed pixels decomposing method respectively. The relationship between land surface temperature and land cover was also studied.
This paper uses PROSAIL model to simulate vegetation canopy reflectance under different chlorophyll contents and Leaf area index (LAI). The changes of NDVIs with different LAIs and chlorophyll contents are analyzed. A simulated spectral dataset was built firstly by using PROSIAL vegetation radiative transfer model with various vegetation chlorophyll concentrations and leaf area index. The responses of NDVIs to LAIs are quantitatively analyzed further based on the dataset. The results show that chlorophyll contents affect canopy reflectance mainly in visible band. Canopy reflectance decreases with an increasing chlorophyll content. Under the same LAI value, NDVI values increase with an increase chlorophyll contents. Under constant content of chlorophyll, NDVIs increases with an increasing LAI. When the value of LAI is less than5, the canopy reflectance is significantly affected by soil background. When value of LAI is higher than5, the earth surface is almost completely covered with vegetation. The increase in LAI has little effect on canopy reflectance and NDVIs consequently. NDVIs increases with the adding of chlorophyll content, when chlorophyll is higher than 40, the rangeability of NDVIs is becoming stable.
The aim of this work was to analyze the distribution of NDVI and its correlation with climatic factors in Eastern China during 1998 - 2008. For this purpose, SPOT-VGT images and 143 meteorological data in Eastern China were collected and analyzed. Results showed that the values of Normalized Difference Vegetation Index (NDVI) were generally higher in the southern part than those in the northern part of Eastern China. The NDVI showed a hidden nonlinear trend after wavelet transform, whereas variations existed in the correlation between NDVI and the four climatic factors (i.e., precipitation and relative humidity, temperature, sunshine hours). NDVI data were positively correlated with temperature and sunshine hours, which was opposite to what was observed in precipitation and relative humidity. Furthermore, the same chan1ge cycle was found for NDVI and precipitation, temperature, and sunshine hours, which were nearly 290 days based on normalized wavelet variance. However, the change cycles of relative humidity showed a different spatial distribution. In the north part of Eastern China, about 30 ten-day were detected, which was not the case for the southern part, where the number increased to 186 ten-day.
The Greenhouse Gases Observing Satellite (GOSAT) can provide high accuracy column-averaged dry air mole fractions of carbon dioxide (XCO2). However, the observations have large gaps due to the impact of the cloud and observational modes. Although kriging interpolation yields the best linear unbiased predictor, it would be computationally expensive for a large dataset. Fixed Rank Kriging (FRK) is based on the Spatial Random Effect (SRE) model and it assumes that the process of interest can be expressed as a linear combination of spatial basis functions, plus a fine-scale-variation component. The FRK predictors and standard errors can be computed rapidly. This paper analyzes the FRK prediction of GOSAT Level 2 XCO2 data over China and shows that the FRK prediction is consistent with other kriging methods (e.g., ordinary kriging). In addition, the result agrees well with the CO2 measurements from the stations at Mt. Waliguan and Shangdianzi.
Carbon dioxide (CO2) is one of major green house gases affecting global climate. Biomass burning caused by fire is an
important emission source of CO2 in the atmosphere. CO2 concentration retrieved from Atmospheric Infrared Sounder
(AIRS) and fire pixel counts (FPC) obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) from 2003
to 2010 over China were obtained and analyzed. The characteristics of correlation between CO and FPC were analyzed
in time series. To investigate the spatial characteristics of correlation between CO2 and fire, energy fires emitted based
on the Global Fire Emissions Database v3 (GFED3) was used. CO2 concentration was steadily increased in both daytime
and nighttime. The seasonal distribution of CO2 concentration and FPC had the similar pattern as the highest value
appeared in Spring and lowest value in Autumn. What’s more, the changes of the aggregated CO2 concentration had a
good agreement with the changes of the total FPC. However, the concentration of CO2 emitted from fires was low except
Heilongjiang province. And the tempo-spatial characteristic of CO2 and FPC were similar with each other. It was
different with characteristic of correlation between CO2 and FPC in whole country.
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