The ecosystem in northeastern China and the Russian Far East is a hotspot of scientific research into the global carbon balance.Forest aboveground biomass(AGB) is an important component in the land surface carbon cycle.In this study,using forest inventory data and forest distribution data,the AGB was estimated for forest in Daxinganlin in northeastern China by combining charge-coupled device(CCD) data from the Small Satellite for Disaster and Environment Monitoring and Forecast(HJ-1) and Geoscience Laser Altimeter System(GLAS) waveform data from the Ice,Cloud and land Elevation Satellite(ICESat).The forest AGB prediction models were separately developed for different forest types in the research area at GLAS footprint level from GLAS waveform parameters and field survey plot biomass in the Changqing(CQ) Forest Center,which was calculated from forest inventory data.The resulted statistical regression models have a R2=0.68 for conifer and R2=0.71 for broadleaf forests.These models were used to estimate biomass for all GLAS footprints of forest located in the study area.All GLAS footprint biomass coupled with various spectral reflectivity parameters and vegetation indices derived from HJ-1 satellite CCD data were used in multiple regression analyses to establish biomass prediction models(R2=0.55 and R2=0.52 for needle and broadleaf respectively).Then the models were used to produce a forest AGB map for the whole study area using the HJ-1 data.Biomass data obtained from forest inventory data of the Zhuanglin(ZL) Forest Center were used as independent field measurements to validate the AGB estimated from HJ-1 CCD data(R2=0.71).About 80% of biomass samples had an error less than 20 t ha-1,and the mean error of all validation samples is 5.74 t ha-1.The pixel-level biomass map was then stratified into different biomass levels to illustrate the AGB spatial distribution pattern in this area.It was found that HJ-1 wide-swath data and GLAS waveform data can be combined to estimate forest biomass with good precision,and the b
GUO ZhiFeng1,CHI Hong1,2 & SUN GuoQing1,3 1State Key Laboratory of Remote Sensing Science,Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University,Institute of Remote Sensing Applications,Chinese Academy of Sciences,Beijing 100101,China
An ensemble method was used to combine three surface soil moisture products,retrieved from passive microwave remote sensing data,to reconstruct a monthly soil moisture data set for China between 2003 and 2010.Using the ensemble data set,the temporal and spatial variations of surface soil moisture were analyzed.The major findings were:(1) The ensemble data set was able to provide more realistic soil moisture information than individual remote sensing products;(2) during the study period,the soil moisture increased in semiarid regions and decreased in arid regions with anoverall drying trend for the whole country;(3) the soil moisture variation trends derived from the three retrieval products and the ensemble data differ from each other but all data sets show the dominant drying trend for the summer,and that most of the drying regions were in major agricultural areas;(4) compared with the precipitation trends derived from Global Precipitation Climatology Project data,it is speculated that climate change is a possible cause for the drying trend in semiarid regions and the wetting trend in arid regions;and (5) combining soil moisture trends with land surface temperature trends derived from Moderate Resolution Imaging Spectroradiomete,the study domain was divided into four categories.Regions with drying and warming trends cover 33.2%,the regions with drying and cooling trends cover 27.4%,the regions with wetting and warming trends cover 21.1% and the regions with wetting and cooling trends cover 18.1%.The first two categories primarily cover the major grain producing areas,while the third category primarily covers nonarable areas such as Northwest China and Tibet.This implies that the moisture and heat variation trends in China are unfavorable to sustainable development and ecology conservation.
The Qinghai-Tibet Plateau plays a very important role in studying severe weather in China and around the globe because of its unique characteristics. Moreover, the surface emissivities of the Qinghai-Tibet Plateau are also important for retrieving surface and atmospheric parameters. In the current study, a retrieval algorithm was developed to retrieve the surface emissivities of the Qinghai-Tibet Plateau. The developed algorithm was derived from the radiative transfer model and was first validated using simulated data from a one-dimensional microwave simulator. The simulated results show good precision. Then, the surface emissivities of the Qinghai-Tibet Plateau were retrieved using brightness temperatures from the advanced microwave-scanning radiometer and atmospheric profile data from the moderate resolution imaging spectroradiometer. Finally, the features of the time and space distribution of the retrieved results were analyzed. In terms of spatial characteristics, a spatial distribution con- sistency was found between the retrieved results and surface coverage types of the Qinghai-Tibet Plateau. In terms of time characteristics, the changes in emissivity, which were within 0.01 for every day, were not evident within a one-month time scale. In addition, surface emissivities are sensitive to rainfall. The reasonability of the retrieved results indicates that the algorithm is feasible. A time-series surface emissivity database on the Qinghai-Tibet Plateau can be built using the developed algorithm, and then other surface or atmospheric parameters would have high retrieval precision to support related geological re- search on the Qinghai-Tibet Plateau.
WANG YongQianSHI JianChengLIU ZhiHongPENG YingJieLIU WenJuan
Long-term highly accurate surface soil moisture data of TP(Tibetan Plateau)are important to the research of Asian monsoon and global atmospheric circulation.However,due to the sparse in-situ networks,the lack of soil moisture observations has seriously hindered the progress of climate change researches of TP.Based on the Dual-Channel soil moisture retrieval algorithm and the satellite observation data of AMSR-E(Advanced Microwave Scanning Radiometer for EOS),we have produced the surface soil moisture data of TP from 2003 to 2010 and analyzed the seasonal characteristic of the soil moisture spatial distribution and its multi-year changing trend in area of TP.Compared to the in-situ observations,the accuracy of the soil moisture retrieved by the proposed algorithm is evaluated.The evaluation result shows that the new soil moisture product has a better accuracy in the TP region than the official product of AMSR-E.The spatial distribution of the annual mean values of soil moisture and the seasonal variations of the monthly-averaged soil moisture are analyzed.The results show that the soil moisture variations in space and time are consistent with the precipitation distribution and the water vapor transmission path in TP.Based on the new soil moisture product,we also analyzed the spatial distribution of the changing trend of multi-year soil moisture in TP.From the comparisons with the precipitation changing trend obtained from the meteorological observation sites in TP,we found that the spatial pattern of the changing trend of soil moisture coincides with the precipitation as a whole.
