Spatiotemporal forecasting of surface soil moisture(SSM)is recognized as a critical scientific issue in precision agricultural irrigation,regional drought monitoring,and early warning systems for extreme precipitation...Spatiotemporal forecasting of surface soil moisture(SSM)is recognized as a critical scientific issue in precision agricultural irrigation,regional drought monitoring,and early warning systems for extreme precipitation.However,long-term forecasting continues to pose formidable challenges because of the complexity observed across both the spatial and temporal scales.In this study,we used a daily SSM dataset at a 0.05°×0.05°spatial resolution over the Qilian Mountains,China and proposed a hybrid Convolutional Long Short-Term Memory(ConvLSTM)-Nudging model,which combined deep neural networks with data assimilation to increase the accuracy of long-term SSM forecasting.We trained and evaluated the SSM predictive performance of four models(Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),ConvLSTM,and ConvLSTM with Squeeze-and-Excitation(SE)attention mechanism(ConvLSTM-SE))in both short-term and long-term scenarios.The results showed that all the models perform well under short-term predictions,but the accuracy decrease substantially in long-term predictions.Therefore,we integrated Nudging technique during the long-term prediction phase to assimilate observational information and rectify model biases.Comprehensive evaluations demonstrate that Nudging significantly improves all the models,with ConvLSTM-Nudging achieving the best performance under the 200-d forecasting scenario.Relative to those of the best-performing ConvLSTM model for long-term forecasts,when observation noiseδ=0.00 and observation fraction obs=50.0%,the coefficient of determination(R2)of ConvLSTM-Nudging increases by approximately 82.1%,while its mean absolute error(MAE)and root mean squared error(RMSE)decrease by approximately 84.8%and 77.3%,respectively;the average Pearson correlation coefficient(r)improves by approximately 23.6%,and Bias is reduced by 98.1%.These results demonstrated that although pure deep learning models achieve high accuracy in the short-term predictions,they are prone to error accumulation and systematic drift in long-term autoregressive predictions.Integrating data assimilation with deep learning and continuously correcting the state through observation can effectively suppress long-term biases,thereby achieving robust long-term SSM forecasting.展开更多
Surface moisture or humidity impacting the lubrication property is a ubiquitous phenomenon in tribological systems,which is demonstrated by a combination of molecular dynamics(MD)simulation and experiment for the orga...Surface moisture or humidity impacting the lubrication property is a ubiquitous phenomenon in tribological systems,which is demonstrated by a combination of molecular dynamics(MD)simulation and experiment for the organic friction modifier(OFM)-containing lubricant.The stearic acid and poly-α-olefin 4cSt(PAO4)were chosen as the OFM and base oil molecules,respectively.The physical adsorption indicates that on the moist surface water molecules are preferentially adsorbed on friction surface,and even make OFM adsorption film thoroughly leave surface and mix with base oil.In shear process,the adsorption of water film and desorption OFM film are further enhanced,particularly under higher shear rate.The simulated friction coefficient(that is proportional to shear rate)increases firstly and then decreases with thickening water film,in good agreement with experiments,while the slip length shows a contrary change.The wear increases with humidity due to tribochemistry revealing the continuous formation and removal of Si–O–Si network.The tribological discrepancy of OFM-containing lubricant in dry and humid conditions is attributed to the slip plane’s transformation from the interface between OFM adsorption film and lubricant bulk to the interface between adsorbed water films.This work provides a new thought to understand the boundary lubrication and failure of lubricant in humid environments,likely water is not always harmful in oil lubrication systems.展开更多
Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables...Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables,including surface soil moisture(SSM),often exhibit nonlinearities that are challenging to identify and quantify using conventional statistical techniques.Therefore,this study presents a hybrid convolutional neural network(CNN)-long short-term memory neural network(LSTM)-attention(CLA)model for predicting RZSM.Owing to the scarcity of soil moisture(SM)observation data,the physical model Hydrus-1D was employed to simulate a comprehensive dataset of spatial-temporal SM.Meteorological data and moderate resolution imaging spectroradiometer vegetation characterization parameters were used as predictor variables for the training and validation of the CLA model.The results of the CLA model for SM prediction in the root zone were significantly enhanced compared with those of the traditional LSTM and CNN-LSTM models.This was particularly notable at the depth of 80–100 cm,where the fitness(R^(2))reached nearly 0.9298.Moreover,the root mean square error of the CLA model was reduced by 49%and 57%compared with those of the LSTM and CNN-LSTM models,respectively.This study demonstrates that the integration of physical modeling and deep learning methods provides a more comprehensive and accurate understanding of spatial-temporal SM variations in the root zone.展开更多
Spatio-temporal dynamic monitoring of soil moisture is highly important to management of agricultural and vegetation eco-systems.The temperature-vegetation dryness index based on the triangle or trapezoid method has b...Spatio-temporal dynamic monitoring of soil moisture is highly important to management of agricultural and vegetation eco-systems.The temperature-vegetation dryness index based on the triangle or trapezoid method has been used widely in previous studies.However,most existing studies simply used linear regression to construct empirical models to fit the edges of the feature space.This requires extensive data from a vast study area,and may lead to subjective results.In this study,a Modified Temperature-Vegetation Dryness Index(MTVDI)was used to monitor surface soil moisture status using MODIS(Moderate-resolution Imaging Spectroradiometer)remote sensing data,in which the dry edge conditions were determined at the pixel scale based on surface energy balance.The MTVDI was validated by field measurements at 30 sites for 10 d and compared with the Temperature-Vegetation Dryness Index(TVDI).The results showed that the R^(2) for MTVDI and soil moisture obviously improved(0.45 for TVDI,0.69 for MTVDI).As for spatial changes,MTVDI can also better reflect the actual soil moisture condition than TVDI.As a result,MTVDI can be considered an effective method to monitor the spatio-temporal changes in surface soil moisture on a regional scale.展开更多
Since the early 2000s, many satellite passive microwave brightness temperature （BT） archives, such as the Advanced Microwave Scanning Radiometer for the Earth Observing System （AMSR-E） BTs, have become the useful ...Since the early 2000s, many satellite passive microwave brightness temperature （BT） archives, such as the Advanced Microwave Scanning Radiometer for the Earth Observing System （AMSR-E） BTs, have become the useful resources for assessing the changes in the surface and deep soil moistures over both arid and semi-arid regions. In this study, we used a new soil effective temperature （T scheme and the archived AMSR-E BTs to estimate surface soil moisture （SM） over the Nagqu region in the central Tibetan Plateau, China. The surface and deep soil temperatures required for the calculation of regional-scale T were obtained from outputs of the Community Land Model version 4.5 （CLM4.5）. In situ SM measurements at the CEOP-CAMP/Tibet （Coordinated Enhanced Observing Period Asia-Australia Monsoon Project on the Tibetan Plateau） experimental sites were used to validate the AMSR-E-based SM estimations at regional and single-site scales. Furthermore, the spatial distribution of monthly mean surface SM over the Nagqu region was obtained from 16 daytime AMSR-E BT observations in July 2004 over the Nagqu region. Results revealed that the AMSR-E-based surface SM estimations agreed well with the in situ-based surface SM measurements, with the root mean square error （RMSE） ranging from 0.042 to 0.066 m3/m3 and the coefficient of determination （R2） ranging from 0.71 to 0.92 during the nighttime and daytime. The regional surface soil water state map showed a clear spatial pattern related to the terrain. It indicated that the lower surface SM values occurred in the mountainous areas of the northern, mid-western and southeastern parts of Nagqu region, while the higher surface SM values appeared in the low elevation areas such as the Tongtian River Basin, Namco Lake and bog meadows in the central part of Nagqu region. Our analysis also showed that the new T^scheme does not require special fitting parameters or additional assumptions, which simplifies the data requirements for regional-scale applications. This scheme combined with the archived satellite passive microwave BT observations can be used to estimate the historical surface SM for hydrological process studies over the Tibetan Plateau regions.展开更多
Marine engineering geology is mainly based on the actual project to study the seabed.This provides a variety of engineering geological parameters for the development of marine engineering(Zhu et al.,2016).This is an e...Marine engineering geology is mainly based on the actual project to study the seabed.This provides a variety of engineering geological parameters for the development of marine engineering(Zhu et al.,2016).This is an early展开更多
Soil moisture is an important parameter that drives agriculture, climate and hydrological systems. In addition, retrieval of soil moisture is important in the analysis as well as its influence on these systems. Radar ...Soil moisture is an important parameter that drives agriculture, climate and hydrological systems. In addition, retrieval of soil moisture is important in the analysis as well as its influence on these systems. Radar imagery is best suited for this retrieval due to its all-weather capability and independence from solar irradiation. Soil moisture retrieval was done for the Malinda Wetland, Tanzania, during two time periods, March and September 2013. The aim of this paper was to analyze soil moisture retrieval performance when vegetation contribution is taken into account. Backscatter values were obtained from TerraSAR-X Spotlight mode imagery taken in March and September 2013. The backscatter values recorded by SAR imagery are influenced by vegetation, soil roughness and soil moisture. Thus, in order to obtain the backscatter due to soil moisture, the roughness and vegetation contribution are determined and decoupled from total backscatter. The roughness parameters were obtained from a Digital Surface Model (DSM) from Unmanned Aerial Vehicle (UAV) photographs whereas the vegetation parameter was obtained by inverting the Water Cloud Model (WCM). Lastly, soil moisture was retrieved using the Oh Model. The coefficient of correlation between the observed and retrieved was 0.39 for the month of March and 0.65 in the month of August. When the vegetation contribution was considered, the r2 for March was 0.64 and that in August was 0.74. The results revealed that accounting for vegetation improved soil moisture retrieval.展开更多
To assess the impacts of temperature and precipitation changes on surface soil moisture CSSM） in the Huang-Huai-Hai Plain （3H） region of China, the approach of conditional nonlinear optimal perturbation related to ...To assess the impacts of temperature and precipitation changes on surface soil moisture CSSM） in the Huang-Huai-Hai Plain （3H） region of China, the approach of conditional nonlinear optimal perturbation related to parameters （CNOP-P） and the Common Land Model are employed. Based on the CNOP-P method and climate change projections derived from 22 global climate models from CMIP5 under a moderate emissions scenario （RCP4.5）, a new climate change scenario that leads to the maximal change magnitudes of SSM is acquired, referred to as the CNOP-P type temperature or precipitation change scenario. Different from the hypothesized climate change scenario, the CNOP-P-type scenario considers the variation of the temperature or precipitation variability. Under the CNOP-P-type temperature change, the SSM changes in the last year of the study period mainly fluctuate in the range from ,0.014 to ＋0.012 m^3 m^-3 （-5.0% to ＋10.0%）, and from ＋0.005 to ＋0.018 m^3 m^-3 （＋1.5% to ＋9.6%） under the CNOP-P-type precipitation change scenario. By analyzing the difference of the SSM changes between different types of climate change scenarios, it is found that this difference associated with SSM is obvious only when precipitation changes are considered. Besides, the greater difference mainly occurs in north of 35°N, where the semi-arid zone is mainly situated. It demonstrates that, in the semi-arid region, SSM is more sensitive to the precipitation variability. Compared with precipitation variability, temperature variability seems to play little role in the variations of SSM.展开更多
Several remotely sensed sea surface salinity（SSS） retrievals with various resolutions from the soil moisture and ocean salinity（SMOS） and Aquarius/SAC-D missions are applied as inputs for retrieving salinity profi...Several remotely sensed sea surface salinity（SSS） retrievals with various resolutions from the soil moisture and ocean salinity（SMOS） and Aquarius/SAC-D missions are applied as inputs for retrieving salinity profiles（S） using multilinear regressions. The performance is evaluated using a total root mean square（RMS） error, different error sources, and the feature resolutions of the retrieved S fields. In the mixed layer of the salinity, the SSS-S regression coefficients are uniformly large. The SSS inputs yield smaller RMS errors in the retrieved S with respect to Argo profiles as their spatial or temporal resolution decreases. The projected SSS errors are dominant, and the retrieved S values are more accurate than those of climatology in the tropics except for the tropical Atlantic, where the regression errors are abnormally large. Below that level, because of the influence of a sea level anomaly, the areas of high-accuracy S values shift to higher latitudes except in the high-latitude southern oceans, where the projected SSS errors are abnormally large. A spectral analysis suggests that the CATDS-0.25° results are much noisier and that the BEC-L4-0.25° results are much smoother than those of the other retrievals. Aquarius-CAP-1° generates the smallest RMS errors, and Aquarius-V2-1° performs well in depicting large-scale phenomena. BEC-L3-0.25°,which has small RMS errors and remarkable mesoscale energy, is the best fit for portraying mesoscale features in the SSS and retrieved S fields. The current priority for retrieving S is to improve the reliability of satellite SSS especially at middle and high latitudes, by developing advanced algorithms, combining both sensors, or weighing between accuracy and resolutions.展开更多
The global mean surface temperature may rise by about 0.3t per decade during the next few decades as a result o f anthropogenic greenhouse gas emissions in the earth's atmosphere. The data generated in the greenho...The global mean surface temperature may rise by about 0.3t per decade during the next few decades as a result o f anthropogenic greenhouse gas emissions in the earth's atmosphere. The data generated in the greenhouse warming simulations (Business-as-Usual scenario of IPCC) with the climate models developed at Max Planck Institute for Meteorology, Hamburg have been used to assess future plausible hydrological scenario for the South Asian region.The model results indicate enhanced surface warming (2.7) for summer and 3.6℃ for winter) over the land reginos of South Asia during the next hundred years. While there is no significant change in the precipitation over most of the land regions during winter, substantial increase in precipitation is likely to occur during summer. As a result, an increase in soil moisture is likely over central india, Bangladesh and South China during summer but a statistically significant decline in soil moisture is expected over central China in winter. A moderate decrease in surface runoff may occur over large areas of central China during winter while the flood prone areas of NE--India, Bangladesh and South China are likely to have an increase ill surface runoff during summer by the end of next century.展开更多
Digitizing the land surface temperature(T_(s))and surface soil moisture(m _(v))is essential for developing the intelligent Digital Earth.Here,we developed a two parameter physical-based passive microwave remote sensin...Digitizing the land surface temperature(T_(s))and surface soil moisture(m _(v))is essential for developing the intelligent Digital Earth.Here,we developed a two parameter physical-based passive microwave remote sensing model for jointly retrieving T_(s) and m_(v) using the dual-polarized T_(b) of Aqua satellite advanced microwave scanning radiometer(AMSR-E)C-band(6.9 GHz)based on the simplified radiative transfer equation.Validation using in situ T_(s) and m_(v) in southern China showed the average root mean square errors(RMSE)of T s and m_(v) retrievals reach 2.42 K(R^(2)=0.61,n=351)and 0.025 g cm^(−3)(R^(2)=0.68,n=663),respectively.The results were also validated using global in situ T_(s)(n=2362)and m_(v)(n=1657)of International Soil Moisture Network.The corresponding RMSE are 3.44 k(R 2=0.86)and 0.039 g cm^(−3)(R^(2)=0.83),respectively.The monthly variations of model-derived Ts and mv are highly consistent with those of the Moderate Resolution Imaging Spectroradiometer T_(s)(R^(2)=0.57;RMSE=2.91 k)and ECV_SM m_(v)(R^(2)=0.51;RMSE=0.045 g cm^(−3)),respectively.Overall,this paper indicates an effective way to jointly modeling T_(s) and m_(v) using passive microwave remote sensing.展开更多
基金funded by the National Natural Science Foundation of China(42461053)the Department of Education of Gansu Province:Higher Education Innovation Fund Project(2023B-064)+1 种基金the Youth Doctoral Fund Project(2024QB-014)the Natural Science Foundation of Gansu Province(25JRRA012).
文摘Spatiotemporal forecasting of surface soil moisture(SSM)is recognized as a critical scientific issue in precision agricultural irrigation,regional drought monitoring,and early warning systems for extreme precipitation.However,long-term forecasting continues to pose formidable challenges because of the complexity observed across both the spatial and temporal scales.In this study,we used a daily SSM dataset at a 0.05°×0.05°spatial resolution over the Qilian Mountains,China and proposed a hybrid Convolutional Long Short-Term Memory(ConvLSTM)-Nudging model,which combined deep neural networks with data assimilation to increase the accuracy of long-term SSM forecasting.We trained and evaluated the SSM predictive performance of four models(Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),ConvLSTM,and ConvLSTM with Squeeze-and-Excitation(SE)attention mechanism(ConvLSTM-SE))in both short-term and long-term scenarios.The results showed that all the models perform well under short-term predictions,but the accuracy decrease substantially in long-term predictions.Therefore,we integrated Nudging technique during the long-term prediction phase to assimilate observational information and rectify model biases.Comprehensive evaluations demonstrate that Nudging significantly improves all the models,with ConvLSTM-Nudging achieving the best performance under the 200-d forecasting scenario.Relative to those of the best-performing ConvLSTM model for long-term forecasts,when observation noiseδ=0.00 and observation fraction obs=50.0%,the coefficient of determination(R2)of ConvLSTM-Nudging increases by approximately 82.1%,while its mean absolute error(MAE)and root mean squared error(RMSE)decrease by approximately 84.8%and 77.3%,respectively;the average Pearson correlation coefficient(r)improves by approximately 23.6%,and Bias is reduced by 98.1%.These results demonstrated that although pure deep learning models achieve high accuracy in the short-term predictions,they are prone to error accumulation and systematic drift in long-term autoregressive predictions.Integrating data assimilation with deep learning and continuously correcting the state through observation can effectively suppress long-term biases,thereby achieving robust long-term SSM forecasting.
基金the financial support from the National Natural Science Foundation of China(52105210)Project funded by China Postdoctoral Science Foundation(2022M712593)+1 种基金Research Fund of the State Key Laboratory of Solidification Processing(NPU)(2021-TS-06)Zhejiang Provincial Natural Science Foundation of China(Key Program,Grant No.LZ21A020001).
文摘Surface moisture or humidity impacting the lubrication property is a ubiquitous phenomenon in tribological systems,which is demonstrated by a combination of molecular dynamics(MD)simulation and experiment for the organic friction modifier(OFM)-containing lubricant.The stearic acid and poly-α-olefin 4cSt(PAO4)were chosen as the OFM and base oil molecules,respectively.The physical adsorption indicates that on the moist surface water molecules are preferentially adsorbed on friction surface,and even make OFM adsorption film thoroughly leave surface and mix with base oil.In shear process,the adsorption of water film and desorption OFM film are further enhanced,particularly under higher shear rate.The simulated friction coefficient(that is proportional to shear rate)increases firstly and then decreases with thickening water film,in good agreement with experiments,while the slip length shows a contrary change.The wear increases with humidity due to tribochemistry revealing the continuous formation and removal of Si–O–Si network.The tribological discrepancy of OFM-containing lubricant in dry and humid conditions is attributed to the slip plane’s transformation from the interface between OFM adsorption film and lubricant bulk to the interface between adsorbed water films.This work provides a new thought to understand the boundary lubrication and failure of lubricant in humid environments,likely water is not always harmful in oil lubrication systems.
基金supported by the National Natural Science Foundation of China(No.42061065)the Third Xinjiang Comprehensive Scientific Expedition,China(No.2022xjkk03010102).
文摘Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables,including surface soil moisture(SSM),often exhibit nonlinearities that are challenging to identify and quantify using conventional statistical techniques.Therefore,this study presents a hybrid convolutional neural network(CNN)-long short-term memory neural network(LSTM)-attention(CLA)model for predicting RZSM.Owing to the scarcity of soil moisture(SM)observation data,the physical model Hydrus-1D was employed to simulate a comprehensive dataset of spatial-temporal SM.Meteorological data and moderate resolution imaging spectroradiometer vegetation characterization parameters were used as predictor variables for the training and validation of the CLA model.The results of the CLA model for SM prediction in the root zone were significantly enhanced compared with those of the traditional LSTM and CNN-LSTM models.This was particularly notable at the depth of 80–100 cm,where the fitness(R^(2))reached nearly 0.9298.Moreover,the root mean square error of the CLA model was reduced by 49%and 57%compared with those of the LSTM and CNN-LSTM models,respectively.This study demonstrates that the integration of physical modeling and deep learning methods provides a more comprehensive and accurate understanding of spatial-temporal SM variations in the root zone.
基金Under the auspices of the National Natural Science Foundation of China(No.41801180)the Natural Science Basic Research Plan in Shaanxi Province of China(No.2020JQ415,2019JQ-767)。
文摘Spatio-temporal dynamic monitoring of soil moisture is highly important to management of agricultural and vegetation eco-systems.The temperature-vegetation dryness index based on the triangle or trapezoid method has been used widely in previous studies.However,most existing studies simply used linear regression to construct empirical models to fit the edges of the feature space.This requires extensive data from a vast study area,and may lead to subjective results.In this study,a Modified Temperature-Vegetation Dryness Index(MTVDI)was used to monitor surface soil moisture status using MODIS(Moderate-resolution Imaging Spectroradiometer)remote sensing data,in which the dry edge conditions were determined at the pixel scale based on surface energy balance.The MTVDI was validated by field measurements at 30 sites for 10 d and compared with the Temperature-Vegetation Dryness Index(TVDI).The results showed that the R^(2) for MTVDI and soil moisture obviously improved(0.45 for TVDI,0.69 for MTVDI).As for spatial changes,MTVDI can also better reflect the actual soil moisture condition than TVDI.As a result,MTVDI can be considered an effective method to monitor the spatio-temporal changes in surface soil moisture on a regional scale.
基金supported by the National Natural Science Foundation of China (41575013)the National Supercomputer Center in Guangzhou, China
文摘Since the early 2000s, many satellite passive microwave brightness temperature （BT） archives, such as the Advanced Microwave Scanning Radiometer for the Earth Observing System （AMSR-E） BTs, have become the useful resources for assessing the changes in the surface and deep soil moistures over both arid and semi-arid regions. In this study, we used a new soil effective temperature （T scheme and the archived AMSR-E BTs to estimate surface soil moisture （SM） over the Nagqu region in the central Tibetan Plateau, China. The surface and deep soil temperatures required for the calculation of regional-scale T were obtained from outputs of the Community Land Model version 4.5 （CLM4.5）. In situ SM measurements at the CEOP-CAMP/Tibet （Coordinated Enhanced Observing Period Asia-Australia Monsoon Project on the Tibetan Plateau） experimental sites were used to validate the AMSR-E-based SM estimations at regional and single-site scales. Furthermore, the spatial distribution of monthly mean surface SM over the Nagqu region was obtained from 16 daytime AMSR-E BT observations in July 2004 over the Nagqu region. Results revealed that the AMSR-E-based surface SM estimations agreed well with the in situ-based surface SM measurements, with the root mean square error （RMSE） ranging from 0.042 to 0.066 m3/m3 and the coefficient of determination （R2） ranging from 0.71 to 0.92 during the nighttime and daytime. The regional surface soil water state map showed a clear spatial pattern related to the terrain. It indicated that the lower surface SM values occurred in the mountainous areas of the northern, mid-western and southeastern parts of Nagqu region, while the higher surface SM values appeared in the low elevation areas such as the Tongtian River Basin, Namco Lake and bog meadows in the central part of Nagqu region. Our analysis also showed that the new T^scheme does not require special fitting parameters or additional assumptions, which simplifies the data requirements for regional-scale applications. This scheme combined with the archived satellite passive microwave BT observations can be used to estimate the historical surface SM for hydrological process studies over the Tibetan Plateau regions.
基金support ed by NSFC Open Research Cruise (Cruise No. NORC2 015-05 and Cruise No. NORC2015-06)funded by Shipti me Sharing Project of NSFC
文摘Marine engineering geology is mainly based on the actual project to study the seabed.This provides a variety of engineering geological parameters for the development of marine engineering(Zhu et al.,2016).This is an early
文摘Soil moisture is an important parameter that drives agriculture, climate and hydrological systems. In addition, retrieval of soil moisture is important in the analysis as well as its influence on these systems. Radar imagery is best suited for this retrieval due to its all-weather capability and independence from solar irradiation. Soil moisture retrieval was done for the Malinda Wetland, Tanzania, during two time periods, March and September 2013. The aim of this paper was to analyze soil moisture retrieval performance when vegetation contribution is taken into account. Backscatter values were obtained from TerraSAR-X Spotlight mode imagery taken in March and September 2013. The backscatter values recorded by SAR imagery are influenced by vegetation, soil roughness and soil moisture. Thus, in order to obtain the backscatter due to soil moisture, the roughness and vegetation contribution are determined and decoupled from total backscatter. The roughness parameters were obtained from a Digital Surface Model (DSM) from Unmanned Aerial Vehicle (UAV) photographs whereas the vegetation parameter was obtained by inverting the Water Cloud Model (WCM). Lastly, soil moisture was retrieved using the Oh Model. The coefficient of correlation between the observed and retrieved was 0.39 for the month of March and 0.65 in the month of August. When the vegetation contribution was considered, the r2 for March was 0.64 and that in August was 0.74. The results revealed that accounting for vegetation improved soil moisture retrieval.
基金provided by the National Natural Science Foundation of China[grant number 91437111],[grant number41375111],[grant number 40830955]
文摘To assess the impacts of temperature and precipitation changes on surface soil moisture CSSM） in the Huang-Huai-Hai Plain （3H） region of China, the approach of conditional nonlinear optimal perturbation related to parameters （CNOP-P） and the Common Land Model are employed. Based on the CNOP-P method and climate change projections derived from 22 global climate models from CMIP5 under a moderate emissions scenario （RCP4.5）, a new climate change scenario that leads to the maximal change magnitudes of SSM is acquired, referred to as the CNOP-P type temperature or precipitation change scenario. Different from the hypothesized climate change scenario, the CNOP-P-type scenario considers the variation of the temperature or precipitation variability. Under the CNOP-P-type temperature change, the SSM changes in the last year of the study period mainly fluctuate in the range from ,0.014 to ＋0.012 m^3 m^-3 （-5.0% to ＋10.0%）, and from ＋0.005 to ＋0.018 m^3 m^-3 （＋1.5% to ＋9.6%） under the CNOP-P-type precipitation change scenario. By analyzing the difference of the SSM changes between different types of climate change scenarios, it is found that this difference associated with SSM is obvious only when precipitation changes are considered. Besides, the greater difference mainly occurs in north of 35°N, where the semi-arid zone is mainly situated. It demonstrates that, in the semi-arid region, SSM is more sensitive to the precipitation variability. Compared with precipitation variability, temperature variability seems to play little role in the variations of SSM.
基金The National Natural Science Foundation of China under contract No.41276088
文摘Several remotely sensed sea surface salinity（SSS） retrievals with various resolutions from the soil moisture and ocean salinity（SMOS） and Aquarius/SAC-D missions are applied as inputs for retrieving salinity profiles（S） using multilinear regressions. The performance is evaluated using a total root mean square（RMS） error, different error sources, and the feature resolutions of the retrieved S fields. In the mixed layer of the salinity, the SSS-S regression coefficients are uniformly large. The SSS inputs yield smaller RMS errors in the retrieved S with respect to Argo profiles as their spatial or temporal resolution decreases. The projected SSS errors are dominant, and the retrieved S values are more accurate than those of climatology in the tropics except for the tropical Atlantic, where the regression errors are abnormally large. Below that level, because of the influence of a sea level anomaly, the areas of high-accuracy S values shift to higher latitudes except in the high-latitude southern oceans, where the projected SSS errors are abnormally large. A spectral analysis suggests that the CATDS-0.25° results are much noisier and that the BEC-L4-0.25° results are much smoother than those of the other retrievals. Aquarius-CAP-1° generates the smallest RMS errors, and Aquarius-V2-1° performs well in depicting large-scale phenomena. BEC-L3-0.25°,which has small RMS errors and remarkable mesoscale energy, is the best fit for portraying mesoscale features in the SSS and retrieved S fields. The current priority for retrieving S is to improve the reliability of satellite SSS especially at middle and high latitudes, by developing advanced algorithms, combining both sensors, or weighing between accuracy and resolutions.
文摘The global mean surface temperature may rise by about 0.3t per decade during the next few decades as a result o f anthropogenic greenhouse gas emissions in the earth's atmosphere. The data generated in the greenhouse warming simulations (Business-as-Usual scenario of IPCC) with the climate models developed at Max Planck Institute for Meteorology, Hamburg have been used to assess future plausible hydrological scenario for the South Asian region.The model results indicate enhanced surface warming (2.7) for summer and 3.6℃ for winter) over the land reginos of South Asia during the next hundred years. While there is no significant change in the precipitation over most of the land regions during winter, substantial increase in precipitation is likely to occur during summer. As a result, an increase in soil moisture is likely over central india, Bangladesh and South China during summer but a statistically significant decline in soil moisture is expected over central China in winter. A moderate decrease in surface runoff may occur over large areas of central China during winter while the flood prone areas of NE--India, Bangladesh and South China are likely to have an increase ill surface runoff during summer by the end of next century.
基金This study was supported by the National Natural Science Foundation of China[grant numbers 31500357,41401055,41430529,41601444]the Natural Science Foundation of Guangdong Province,China[grant numbers 2014A030310233,2015A030313809,2015A030313811]+4 种基金the Science and Technology Plan Fund of Guangzhou City,China[grant numbers 201510010240,201610010134]the Water Resource Science and Technology Innovation Program of Guangdong Province[grant numbers 2016-16,2015-14]the Scientific Platform and Innovation Capability Construction Program of GDAS[2016GDASPT-0210]the High-Level Leading Talent Introduction Program of GDAS[2016GDASRC-0101]Fujian Collaborative Innovation Center for Big Data Applications in Governments.
文摘Digitizing the land surface temperature(T_(s))and surface soil moisture(m _(v))is essential for developing the intelligent Digital Earth.Here,we developed a two parameter physical-based passive microwave remote sensing model for jointly retrieving T_(s) and m_(v) using the dual-polarized T_(b) of Aqua satellite advanced microwave scanning radiometer(AMSR-E)C-band(6.9 GHz)based on the simplified radiative transfer equation.Validation using in situ T_(s) and m_(v) in southern China showed the average root mean square errors(RMSE)of T s and m_(v) retrievals reach 2.42 K(R^(2)=0.61,n=351)and 0.025 g cm^(−3)(R^(2)=0.68,n=663),respectively.The results were also validated using global in situ T_(s)(n=2362)and m_(v)(n=1657)of International Soil Moisture Network.The corresponding RMSE are 3.44 k(R 2=0.86)and 0.039 g cm^(−3)(R^(2)=0.83),respectively.The monthly variations of model-derived Ts and mv are highly consistent with those of the Moderate Resolution Imaging Spectroradiometer T_(s)(R^(2)=0.57;RMSE=2.91 k)and ECV_SM m_(v)(R^(2)=0.51;RMSE=0.045 g cm^(−3)),respectively.Overall,this paper indicates an effective way to jointly modeling T_(s) and m_(v) using passive microwave remote sensing.
