基于ERS散射计数据的青藏高原土壤水分估算方法研究
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摘要
土壤水分在全球水资源中所占的比列很小,存储在土壤空隙中的水分仅占全球水资源0.005%,但它是水文模型和气候模型的重要边界条件。青藏高原作为世界上平均海拔最高、面积最大、地形最为复杂的高原,其能量与水分循环过程对亚洲季风、东亚大气环流及全球气候变化均有极大的影响。准确持续地获取青藏高原地表土壤湿度数据一直是科学家研究青藏高原地气相互作用过程的努力方向之一。
本文的是基于ERS-1/2风散射计数据进行青藏高原地表土壤湿度参数反演。为获得较高的反演精度,本文引入一个简化的地表散射模型,然后通过最新发展的裸露地表随机散射理论模型对其参数进行标定。根据散射计的数据特点,通过寻找不同入射角下简单地表散射模型的参数之间的函数关系,来减少反演模型中的未知量,得到基于ERS-1/2散射计数据的裸露地表土壤水分估算方法,然后选择和计算植被散射水云模型的植被参数,在此基础上,根据青藏高原的土地利用信息,定量估算植被散射从而进行植被影响校正,最后反演得到进行青藏高原全年地表土壤水分的时空分布数据。
为建立基于ERS-1/2散射计的裸露地表土壤水分反演模型,本文首先对比分析不同的裸露地表散射模型和经验半经验模型进行青藏高原地表散射模拟时,模拟结果与大尺度的ERS-1/2风散射计的观测值之间误差;论证了在进行大尺度地表散射模拟时,AIEM模型仍然比其他地表散射模型有更高的精度,并且地表自相关函数选择指数相关函数时,AIEM模型的模拟值与ERS-1/2风散射计的观测值最为接近;通过引入一个简化的裸露地表后向散射模型(SSM),并利用AIEM模型模拟建立的地表散射特性数据对SSM进行标定,从而建立散射计前、中天线不同入射角下SSM模型中的参数A_m和A_f之间的函数关系以及参数b与入射角的关系,得到可以直接利用散射计的每次观测(两个不同入射角)进行裸露地表土壤水分反演的方法;根据GAME-Tibet观测场的地表实测数据,运用上述模型基于ERS散射计数据得到的观测场的裸露地表土壤水分,结果表明,反演值能够较好的反映土壤水分的变化特征,反演值与实测值之间的相关性能够达到0.6-0.7,标准偏差约0.04cm~3/cm~3
为进一步减少植被覆盖下土壤水分反演时的未知参数,本文根据青藏高原
Although soil moisture constitutes only about 0.005 % of the global water resources, it's important as a boundary condition for hydrologic and climate models. Tibetan Plateau is the largest and highest area with complex landform in the world, whose water and energy cycle play a crucial role on the Asia monsoon, East Asia atmosphere general circulation and global climate change. Retrieving nice continuant soil moisture data on Tibetan Plateau is one of the most important work for scientists who focus their studies on the interactions between land and atmosphere of Tibetan Plateau.This study focuses on the development of a consistant methodology for soil moisture inversion in Tibetan Plateau from ERS-1/2 Wind SCatterometer data(WSC). To gain a higher precision of soil moisture data, we firstly introduced a simplified scat-tering(SSM) model calibrated by the latest developed Advanced Integral Equation Model(AIEM) which predicts the backscattering coefficient of random rough surface. In light of the characteristics of WSC data in incident angle, we built the relation between the roughness parameters of SSM from two observations with different incident angles to reduce unknown parameters in retrieving process, thus we could derive the surface soil moisture over bare soil from two simultaneous observation from fore and mid beam of WSC respectively. Secondly, we tried to revise (?)e vegetation effect on the soil moisture estimation. We used the Water-Cloud vegetation model for estimating vegetation effects quantitatively with the reasonablly estimated canopy parameters for each vegetation canopy on Tibetan Plateau. Lastly, we derived the spatial and time series soil moisture data of Tibetan Plateau and the analysis of the results was carried out.To develop our soil moisture retrieval model over bare soil with WSC data, the validity of different theoretical scattering models over random rough surface and empirical and semi-empirical retrieval models over bare soil were investigated with WSC data over Tibetan Plateau. The results indicated AIEM could simulate the surface backscattering with higher precision than other models and exponential autocorrela-
tion function would be more suitable than other autocorrelation function when AIEM simulation was conducted over large-scale area and Tibetan Plateau. Because AIEM model is so complex that we cann't use it in soil moisture retrieval without any simplification, we then introduced a simplification scattering model, after calibrated by AIEM model, the correlation function between the roughness parameter "A" of SSM with two different incident angle of WSC were analyzed. The parameter "b", on the other hand, can be regressed by radar incident angle. Thus we can retrieve the bare soil moisture only with two observation data from fore and mid beam of WSC respectively. The above retrieval method performed well in Tibetan Plateau with WSC data, and the results were cross-validated with the measured soil moisture data collected from GAME-Tibet IOP98. The correlation coefficient between the estimated and measured soil moisture reached about 0.6 to 0.7, the stand deviation of estimated soil moisture was of the order of 0.04 cm3/cm3.If we want retrieve land surface soil moisture under vegetation cover, more vegetation .parameter should be known or the unknown parameters should be reduced. Because of the obvious seasonal characteristics of precipitation and frozen-thawed soil, WSC data between Apr. 15th and May 20th of each year in each observation cell was chosen to retrieve parameter "A" of SSM, and these "A" were regressed with incident angle, so we could get the value of parameter "A" of SSM for each observation cell of WSC in each year. Retrieval results with above method in GAME-Tibet experiment sites demonstrated the retrieved soil moisture by two different method, one was the derived A from two observation, the other was from regressed "A", were equivalent, but the derived soil moisture data with regressed A was more smooth with changing time series than those with derived "A" from two observation.When revising the vegetation scattering effects on soil moisture retrieval, we firstly corrected the value of vegetation parameter "b" by the relation of "b" between two different wave length, namely C-band and L-band electromagnetic wave. Secondly, we estimated the vegetation water content(Wc) using AVHRR-NDVI using the empirical relation between Wc and NDVI. At last, we determined the value of vegetation backscattering, and the vegetation effects were explicitly incorporated in our retrieval model.
本文的是基于ERS-1/2风散射计数据进行青藏高原地表土壤湿度参数反演。为获得较高的反演精度,本文引入一个简化的地表散射模型,然后通过最新发展的裸露地表随机散射理论模型对其参数进行标定。根据散射计的数据特点,通过寻找不同入射角下简单地表散射模型的参数之间的函数关系,来减少反演模型中的未知量,得到基于ERS-1/2散射计数据的裸露地表土壤水分估算方法,然后选择和计算植被散射水云模型的植被参数,在此基础上,根据青藏高原的土地利用信息,定量估算植被散射从而进行植被影响校正,最后反演得到进行青藏高原全年地表土壤水分的时空分布数据。
为建立基于ERS-1/2散射计的裸露地表土壤水分反演模型,本文首先对比分析不同的裸露地表散射模型和经验半经验模型进行青藏高原地表散射模拟时,模拟结果与大尺度的ERS-1/2风散射计的观测值之间误差;论证了在进行大尺度地表散射模拟时,AIEM模型仍然比其他地表散射模型有更高的精度,并且地表自相关函数选择指数相关函数时,AIEM模型的模拟值与ERS-1/2风散射计的观测值最为接近;通过引入一个简化的裸露地表后向散射模型(SSM),并利用AIEM模型模拟建立的地表散射特性数据对SSM进行标定,从而建立散射计前、中天线不同入射角下SSM模型中的参数A_m和A_f之间的函数关系以及参数b与入射角的关系,得到可以直接利用散射计的每次观测(两个不同入射角)进行裸露地表土壤水分反演的方法;根据GAME-Tibet观测场的地表实测数据,运用上述模型基于ERS散射计数据得到的观测场的裸露地表土壤水分,结果表明,反演值能够较好的反映土壤水分的变化特征,反演值与实测值之间的相关性能够达到0.6-0.7,标准偏差约0.04cm~3/cm~3
为进一步减少植被覆盖下土壤水分反演时的未知参数,本文根据青藏高原
Although soil moisture constitutes only about 0.005 % of the global water resources, it's important as a boundary condition for hydrologic and climate models. Tibetan Plateau is the largest and highest area with complex landform in the world, whose water and energy cycle play a crucial role on the Asia monsoon, East Asia atmosphere general circulation and global climate change. Retrieving nice continuant soil moisture data on Tibetan Plateau is one of the most important work for scientists who focus their studies on the interactions between land and atmosphere of Tibetan Plateau.This study focuses on the development of a consistant methodology for soil moisture inversion in Tibetan Plateau from ERS-1/2 Wind SCatterometer data(WSC). To gain a higher precision of soil moisture data, we firstly introduced a simplified scat-tering(SSM) model calibrated by the latest developed Advanced Integral Equation Model(AIEM) which predicts the backscattering coefficient of random rough surface. In light of the characteristics of WSC data in incident angle, we built the relation between the roughness parameters of SSM from two observations with different incident angles to reduce unknown parameters in retrieving process, thus we could derive the surface soil moisture over bare soil from two simultaneous observation from fore and mid beam of WSC respectively. Secondly, we tried to revise (?)e vegetation effect on the soil moisture estimation. We used the Water-Cloud vegetation model for estimating vegetation effects quantitatively with the reasonablly estimated canopy parameters for each vegetation canopy on Tibetan Plateau. Lastly, we derived the spatial and time series soil moisture data of Tibetan Plateau and the analysis of the results was carried out.To develop our soil moisture retrieval model over bare soil with WSC data, the validity of different theoretical scattering models over random rough surface and empirical and semi-empirical retrieval models over bare soil were investigated with WSC data over Tibetan Plateau. The results indicated AIEM could simulate the surface backscattering with higher precision than other models and exponential autocorrela-
tion function would be more suitable than other autocorrelation function when AIEM simulation was conducted over large-scale area and Tibetan Plateau. Because AIEM model is so complex that we cann't use it in soil moisture retrieval without any simplification, we then introduced a simplification scattering model, after calibrated by AIEM model, the correlation function between the roughness parameter "A" of SSM with two different incident angle of WSC were analyzed. The parameter "b", on the other hand, can be regressed by radar incident angle. Thus we can retrieve the bare soil moisture only with two observation data from fore and mid beam of WSC respectively. The above retrieval method performed well in Tibetan Plateau with WSC data, and the results were cross-validated with the measured soil moisture data collected from GAME-Tibet IOP98. The correlation coefficient between the estimated and measured soil moisture reached about 0.6 to 0.7, the stand deviation of estimated soil moisture was of the order of 0.04 cm3/cm3.If we want retrieve land surface soil moisture under vegetation cover, more vegetation .parameter should be known or the unknown parameters should be reduced. Because of the obvious seasonal characteristics of precipitation and frozen-thawed soil, WSC data between Apr. 15th and May 20th of each year in each observation cell was chosen to retrieve parameter "A" of SSM, and these "A" were regressed with incident angle, so we could get the value of parameter "A" of SSM for each observation cell of WSC in each year. Retrieval results with above method in GAME-Tibet experiment sites demonstrated the retrieved soil moisture by two different method, one was the derived A from two observation, the other was from regressed "A", were equivalent, but the derived soil moisture data with regressed A was more smooth with changing time series than those with derived "A" from two observation.When revising the vegetation scattering effects on soil moisture retrieval, we firstly corrected the value of vegetation parameter "b" by the relation of "b" between two different wave length, namely C-band and L-band electromagnetic wave. Secondly, we estimated the vegetation water content(Wc) using AVHRR-NDVI using the empirical relation between Wc and NDVI. At last, we determined the value of vegetation backscattering, and the vegetation effects were explicitly incorporated in our retrieval model.
引文
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