高分辨率地表冻融监测对研究根河地区碳氮循环、水土流失和土壤冻融侵蚀非常重要。本文采用Kou等(2017)提出的被动微波亮温降尺度方法和1 km空间分辨率的温度数据,将0.25°空间分辨率的被动微波亮温降尺度至0.01°空间分辨率。...高分辨率地表冻融监测对研究根河地区碳氮循环、水土流失和土壤冻融侵蚀非常重要。本文采用Kou等(2017)提出的被动微波亮温降尺度方法和1 km空间分辨率的温度数据,将0.25°空间分辨率的被动微波亮温降尺度至0.01°空间分辨率。利用通过模型模拟与实验数据发展得到的冻融判别式算法DFA_Zhao(Discriminant Function Algorithm)和改进的冻融判别式算法DFA_Kou(Improved Discriminant Function Algorithm),基于降尺度前后的被动微波亮温监测根河地区的地表冻融。以根河地区2013年7月-2015年12月的地下0-5 cm深度的实测土壤温度检验这两种冻融判识算法的分类精度。结果显示,降尺度前后两种冻融判识算法整体判对率差异在6.72%内;DFA_Zhao算法融化判对率的均值比DFA_Kou算法高10%,DFA_Kou算法冻结判对率均值比DFA_Zhao算法高1%。两种冻融判别式算法的冻结判对率均在90%以上,升轨期的融化判对率均在80%以上,但两算法降轨期的融化判对率较低,在40%-82%之间。同时,还进一步讨论并分析了两种冻融判别式算法和被动微波亮温降尺度方法可能存在的问题,指出了可能的改进方向。展开更多
Highly accurate observations at various scales on the land surface are urgently needed for the studies of many areas,such as hydrology,meteorology,and agriculture.With the rapid development of remote sensing technique...Highly accurate observations at various scales on the land surface are urgently needed for the studies of many areas,such as hydrology,meteorology,and agriculture.With the rapid development of remote sensing techniques,remote sensing has had the capacity of monitoring many factors of the Earth's land surface.Especially,the space-borne microwave remote sensing systems have been widely used in the quantitative monitoring of global snow,soil moisture,and vegetation parameters with their all-weather,all-time observation capabilities and their sensitivities to the characteristics of land surface factors.Based on the electromagnetic theories and microwave radiative transfer equations,researchers have achieved great successes in the microwave remote sensing studies for different sensors in recent years.This article has systematically reviewed the progresses on five research areas including microwave theoretical modeling,microwave inversion on soil moisture,snow,vegetation and land surface temperatures.Through the further enrichment of remote sensing datasets and the development of remote sensing theories and inversion techniques,remote sensing including microwave remote sensing will play a more important role in the studies and applications of the Earth systems.展开更多
The primary objective of this work is to develop an operational snow depth retrieval algorithm for the FengYun3B Microwave Radiation Imager(FY3B-MWRI)in China.Based on 7-year(2002–2009)observations of brightness temp...The primary objective of this work is to develop an operational snow depth retrieval algorithm for the FengYun3B Microwave Radiation Imager(FY3B-MWRI)in China.Based on 7-year(2002–2009)observations of brightness temperature by the Advanced Microwave Scanning Radiometer-EOS(AMSR-E)and snow depth from Chinese meteorological stations,we develop a semi-empirical snow depth retrieval algorithm.When its land cover fraction is larger than 85%,we regard a pixel as pure at the satellite passive microwave remote-sensing scale.A 1-km resolution land use/land cover(LULC)map from the Data Center for Resources and Environmental Sciences,Chinese Academy of Sciences,is used to determine fractions of four main land cover types(grass,farmland,bare soil,and forest).Land cover sensitivity snow depth retrieval algorithms are initially developed using AMSR-E brightness temperature data.Each grid-cell snow depth was estimated as the sum of snow depths from each land cover algorithm weighted by percentages of land cover types within each grid cell.Through evaluation of this algorithm using station measurements from 2006,the root mean square error(RMSE)of snow depth retrieval is about 5.6 cm.In forest regions,snow depth is underestimated relative to ground observation,because stem volume and canopy closure are ignored in current algorithms.In addition,comparison between snow cover derived from AMSR-E and FY3B-MWRI with Moderate-resolution Imaging Spectroradiometer(MODIS)snow cover products(MYD10C1)in January 2010 showed that algorithm accuracy in snow cover monitoring can reach 84%.Finally,we compared snow water equivalence(SWE)derived using FY3B-MWRI with AMSR-E SWE products in the Northern Hemisphere.The results show that AMSR-E overestimated SWE in China,which agrees with other validations.展开更多
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 ...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.展开更多
Accurate quantitative global scale snow water equivalent information is crucial for meteorology, hydrology, water cycle and global change studies, and is of great importance for snow melt-runoff forecast, water resour...Accurate quantitative global scale snow water equivalent information is crucial for meteorology, hydrology, water cycle and global change studies, and is of great importance for snow melt-runoff forecast, water resources management and flood control. With land surface process model and snow process model, the snow water equivalent can be simulated with certain accuracy, with the forcing data as input. However, the snow water equivalent simulated using the snow process models has large uncertainties spatially and temporally, and it may be far from the needs of practical applications. Thus, the large scale snow water equivalent information is mainly from remote sensing. Beginning with the launch of Nimbus-7 satellite, the research on microwave snow water equivalent remote sensing has developed for more than 30 years, researchers have made progress in many aspects, including the electromagnetic scattering and emission modeling, ground and airborne experiments, and inversion algorithms for future global high resolution snow water equivalent remote sensing program. In this paper, the research and progress in the aspects of electromagnetic scattering/emission modeling over snow covered terrain and snow water equivalent inversion algorithm will be summarized.展开更多
In this paper,we studied the effect of spatial distribution of soil parameters on passive soil moisture retrieval at pixel scale.First,we evaluated the forward microwave emission model and soil moisture retrieval algo...In this paper,we studied the effect of spatial distribution of soil parameters on passive soil moisture retrieval at pixel scale.First,we evaluated the forward microwave emission model and soil moisture retrieval algorithm accuracy through the observa-tion of field experiments.Then,we used soil parameters in different spatial distribution patterns,including random,normal,and uniform distribution,to determine the different levels of heterogeneity on soil moisture retrieval,in order to seek the rela-tionship between heterogeneity and soil moisture retrieval error.Finally,we conducted a controlled heterogeneity effect ex-periment measurements using a Truck-mounted Multi-frequency Radiometer(TMMR) to validate our simulation results.This work has proved that the soil moisture retrieval algorithm had a high accuracy(RMSE=0.049 cm3 cm 3) and can satisfy the need of this research.The simulation brightness temperatures match well with observations,with RMSE=9.89 K.At passive microwave remote sensing pixel scale,soil parameters with different spatial distribution patterns could have different levels of error on soil moisture estimation.Overall,we found that soil moisture with a random distribution in a satellite pixel scale can cause the largest error,with a normal distribution being the second,and a uniform distribution the least due to the smallest het-erogeneity.展开更多
Microwave radiometers have many applications because of their penetration ability. However, two major problems remain that obstruct the development of microwave research. One factor that limits their commercial applic...Microwave radiometers have many applications because of their penetration ability. However, two major problems remain that obstruct the development of microwave research. One factor that limits their commercial application is the relatively low resolution of microwave radiometers. The other is the non-uniform spatial resolution for each frequency of the radiometer. The resolution mismatch becomes a critical consideration when observations at two or more frequencies must be combined. In this paper, we have used the Backus-Gilbert method to solve these two problems, while AMSR-E is chosen as the research object. First, we derived the Backus-Gilbert method in detail. The simulated data were then used to decide the optimum parameters in the Backus-Gilbert method. To enhance the resolution, the Backus-Gilbert method has been applied to the AMSR-E data, which covered the Mexico Gulf and the Amazon River. After resolution was enhanced, detailed information was obtained and compared with visible high resolution data. To match the resolution, the AMSR-E data from the Oklahoma Little Washed were used to compute the Microwave Vegetation Index (MVI), which was developed by J. C. Shi. Compared to the original MVIs, the information contained in the MVIs that were processed by the Backus-Gilbert method is more reliable.展开更多
文摘高分辨率地表冻融监测对研究根河地区碳氮循环、水土流失和土壤冻融侵蚀非常重要。本文采用Kou等(2017)提出的被动微波亮温降尺度方法和1 km空间分辨率的温度数据,将0.25°空间分辨率的被动微波亮温降尺度至0.01°空间分辨率。利用通过模型模拟与实验数据发展得到的冻融判别式算法DFA_Zhao(Discriminant Function Algorithm)和改进的冻融判别式算法DFA_Kou(Improved Discriminant Function Algorithm),基于降尺度前后的被动微波亮温监测根河地区的地表冻融。以根河地区2013年7月-2015年12月的地下0-5 cm深度的实测土壤温度检验这两种冻融判识算法的分类精度。结果显示,降尺度前后两种冻融判识算法整体判对率差异在6.72%内;DFA_Zhao算法融化判对率的均值比DFA_Kou算法高10%,DFA_Kou算法冻结判对率均值比DFA_Zhao算法高1%。两种冻融判别式算法的冻结判对率均在90%以上,升轨期的融化判对率均在80%以上,但两算法降轨期的融化判对率较低,在40%-82%之间。同时,还进一步讨论并分析了两种冻融判别式算法和被动微波亮温降尺度方法可能存在的问题,指出了可能的改进方向。
基金supported by National Natural Science Foundation of China(Grant Nos. 40930530 and 40901180)
文摘Highly accurate observations at various scales on the land surface are urgently needed for the studies of many areas,such as hydrology,meteorology,and agriculture.With the rapid development of remote sensing techniques,remote sensing has had the capacity of monitoring many factors of the Earth's land surface.Especially,the space-borne microwave remote sensing systems have been widely used in the quantitative monitoring of global snow,soil moisture,and vegetation parameters with their all-weather,all-time observation capabilities and their sensitivities to the characteristics of land surface factors.Based on the electromagnetic theories and microwave radiative transfer equations,researchers have achieved great successes in the microwave remote sensing studies for different sensors in recent years.This article has systematically reviewed the progresses on five research areas including microwave theoretical modeling,microwave inversion on soil moisture,snow,vegetation and land surface temperatures.Through the further enrichment of remote sensing datasets and the development of remote sensing theories and inversion techniques,remote sensing including microwave remote sensing will play a more important role in the studies and applications of the Earth systems.
基金supported by the National Natural Science Foundation of China(Grant Nos.41171260&41030534)
文摘The primary objective of this work is to develop an operational snow depth retrieval algorithm for the FengYun3B Microwave Radiation Imager(FY3B-MWRI)in China.Based on 7-year(2002–2009)observations of brightness temperature by the Advanced Microwave Scanning Radiometer-EOS(AMSR-E)and snow depth from Chinese meteorological stations,we develop a semi-empirical snow depth retrieval algorithm.When its land cover fraction is larger than 85%,we regard a pixel as pure at the satellite passive microwave remote-sensing scale.A 1-km resolution land use/land cover(LULC)map from the Data Center for Resources and Environmental Sciences,Chinese Academy of Sciences,is used to determine fractions of four main land cover types(grass,farmland,bare soil,and forest).Land cover sensitivity snow depth retrieval algorithms are initially developed using AMSR-E brightness temperature data.Each grid-cell snow depth was estimated as the sum of snow depths from each land cover algorithm weighted by percentages of land cover types within each grid cell.Through evaluation of this algorithm using station measurements from 2006,the root mean square error(RMSE)of snow depth retrieval is about 5.6 cm.In forest regions,snow depth is underestimated relative to ground observation,because stem volume and canopy closure are ignored in current algorithms.In addition,comparison between snow cover derived from AMSR-E and FY3B-MWRI with Moderate-resolution Imaging Spectroradiometer(MODIS)snow cover products(MYD10C1)in January 2010 showed that algorithm accuracy in snow cover monitoring can reach 84%.Finally,we compared snow water equivalence(SWE)derived using FY3B-MWRI with AMSR-E SWE products in the Northern Hemisphere.The results show that AMSR-E overestimated SWE in China,which agrees with other validations.
基金supported by the National High-tech R&D Program of China(Grant No.2012AA12A304)the National Natural Science Foundation of China(Grant No.40930530)
文摘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.
基金funded by the Strategic Priority Research Program for Space Sciences(Grant No.XDA04061200)of the Chinese Academy of SciencesNational Basic Research Program of China(Grant No.2015CB953701)
文摘Accurate quantitative global scale snow water equivalent information is crucial for meteorology, hydrology, water cycle and global change studies, and is of great importance for snow melt-runoff forecast, water resources management and flood control. With land surface process model and snow process model, the snow water equivalent can be simulated with certain accuracy, with the forcing data as input. However, the snow water equivalent simulated using the snow process models has large uncertainties spatially and temporally, and it may be far from the needs of practical applications. Thus, the large scale snow water equivalent information is mainly from remote sensing. Beginning with the launch of Nimbus-7 satellite, the research on microwave snow water equivalent remote sensing has developed for more than 30 years, researchers have made progress in many aspects, including the electromagnetic scattering and emission modeling, ground and airborne experiments, and inversion algorithms for future global high resolution snow water equivalent remote sensing program. In this paper, the research and progress in the aspects of electromagnetic scattering/emission modeling over snow covered terrain and snow water equivalent inversion algorithm will be summarized.
基金supported by National Natural Science Foun-dation of China (Grant No.41030534)National Basic Research Program of China (Grant No. 2007CB714403)The European Commission Under FP7 Topic ENV.2007.4.1.4.2 "Improving Observing Systems for Water Resource Management"
文摘In this paper,we studied the effect of spatial distribution of soil parameters on passive soil moisture retrieval at pixel scale.First,we evaluated the forward microwave emission model and soil moisture retrieval algorithm accuracy through the observa-tion of field experiments.Then,we used soil parameters in different spatial distribution patterns,including random,normal,and uniform distribution,to determine the different levels of heterogeneity on soil moisture retrieval,in order to seek the rela-tionship between heterogeneity and soil moisture retrieval error.Finally,we conducted a controlled heterogeneity effect ex-periment measurements using a Truck-mounted Multi-frequency Radiometer(TMMR) to validate our simulation results.This work has proved that the soil moisture retrieval algorithm had a high accuracy(RMSE=0.049 cm3 cm 3) and can satisfy the need of this research.The simulation brightness temperatures match well with observations,with RMSE=9.89 K.At passive microwave remote sensing pixel scale,soil parameters with different spatial distribution patterns could have different levels of error on soil moisture estimation.Overall,we found that soil moisture with a random distribution in a satellite pixel scale can cause the largest error,with a normal distribution being the second,and a uniform distribution the least due to the smallest het-erogeneity.
基金supported by Chinese Special Funds for National Basic Research Project of China (Grant No. 2007CB714403)National High Technology Research and Development Program of China (Grant Nos. 2007AA12Z135, 2008AA12Z110)Chinese Academy of Sciences (Grant No. KZCX2-YW-Q10-2)
文摘Microwave radiometers have many applications because of their penetration ability. However, two major problems remain that obstruct the development of microwave research. One factor that limits their commercial application is the relatively low resolution of microwave radiometers. The other is the non-uniform spatial resolution for each frequency of the radiometer. The resolution mismatch becomes a critical consideration when observations at two or more frequencies must be combined. In this paper, we have used the Backus-Gilbert method to solve these two problems, while AMSR-E is chosen as the research object. First, we derived the Backus-Gilbert method in detail. The simulated data were then used to decide the optimum parameters in the Backus-Gilbert method. To enhance the resolution, the Backus-Gilbert method has been applied to the AMSR-E data, which covered the Mexico Gulf and the Amazon River. After resolution was enhanced, detailed information was obtained and compared with visible high resolution data. To match the resolution, the AMSR-E data from the Oklahoma Little Washed were used to compute the Microwave Vegetation Index (MVI), which was developed by J. C. Shi. Compared to the original MVIs, the information contained in the MVIs that were processed by the Backus-Gilbert method is more reliable.
