The spatial-temporal evolution of coherent structures （CS） is significant for turbulence control and drag re- duction. Among the CS, low and high speed streak structures show typical burst phenomena. The analysis wa...The spatial-temporal evolution of coherent structures （CS） is significant for turbulence control and drag re- duction. Among the CS, low and high speed streak structures show typical burst phenomena. The analysis was based on a time series of three-dimensional and three-component （3D-3C） velocity fields of the flat plate turbulent boundary layer （TBL） measured by a Tomographic and Time-resolved PIV （Tomo TRPIV） system. Using multi-resolution wavelet transform and conditional sampling method, we extracted the intrinsic topologies and found that the streak structures appear in bar-like patterns. Furthermore, we seized locations and velocity information of transient CS, and then calculated the propagation velocity of CS based on spatial-temporal cross-correlation scanning. This laid a foundation for further studies on relevant dynamics properties.展开更多
While data like HJ-1 CCD images have advantageous spatial characteristics for describing crop properties,the temporal resolution of the data is rather low,which can be easily made worse by cloud contamination.In contr...While data like HJ-1 CCD images have advantageous spatial characteristics for describing crop properties,the temporal resolution of the data is rather low,which can be easily made worse by cloud contamination.In contrast,although Moderate Resolution Imaging Spectroradiometer(MODIS)can only achieve a spatial resolution of 250 m in its normalised difference vegetation index(NDVI)product,it has a high temporal resolution,covering the Earth up to multiple times per day.To combine the high spatial resolution and high temporal resolution of different data sources,a new method(Spatial and Temporal Adaptive Vegetation index Fusion Model[STAVFM])for blending NDVI of different spatial and temporal resolutions to produce high spatialtemporal resolution NDVI datasets was developed based on Spatial and Temporal Adaptive Reflectance Fusion Model(STARFM).STAVFM defines a time window according to the temporal variation of crops,takes crop phenophase into consideration and improves the temporal weighting algorithm.The result showed that the new method can combine the temporal information of MODIS NDVI and spatial difference information of HJ-1 CCD NDVI to generate an NDVI dataset with both high spatial and high temporal resolution.An application of the generated NDVI dataset in crop biomass estimation was provided.An average absolute error of 17.2%was achieved.The estimated winter wheat biomass correlated well with observed biomass(R^(2) of 0.876).We conclude that the new dataset will improve the application of crop biomass estimation by describing the crop biomass accumulation in detail.There is potential to apply the approach in many other studies,including crop production estimation,crop growth monitoring and agricultural ecosystem carbon cycle research,which will contribute to the implementation of Digital Earth by describing land surface processes in detail.展开更多
High spatial resolution and high temporal frequency fractional vegetation cover(FVC) products have been increasingly in demand to monitor and research land surface processes. This paper develops an algorithm to estima...High spatial resolution and high temporal frequency fractional vegetation cover(FVC) products have been increasingly in demand to monitor and research land surface processes. This paper develops an algorithm to estimate FVC at a 30-m/15-day resolution over China by taking advantage of the spatial and temporal information from different types of sensors: the 30-m resolution sensor on the Chinese environment satellite(HJ-1) and the 1-km Moderate Resolution Imaging Spectroradiometer(MODIS). The algorithm was implemented for each main vegetation class and each land cover type over China. First, the high spatial resolution and high temporal frequency normalized difference vegetation index(NDVI) was acquired by using the continuous correction(CC) data assimilation method. Then, FVC was generated with a nonlinear pixel unmixing model. Model coefficients were obtained by statistical analysis of the MODIS NDVI. The proposed method was evaluated based on in situ FVC measurements and a global FVC product(GEOV1 FVC). Direct validation using in situ measurements at 97 sampling plots per half month in 2010 showed that the annual mean errors(MEs) of forest, cropland, and grassland were-0.025, 0.133, and 0.160, respectively, indicating that the FVCs derived from the proposed algorithm were consistent with ground measurements [R2 = 0.809,root-mean-square deviation(RMSD) = 0.065]. An intercomparison between the proposed FVC and GEOV1 FVC demonstrated that the two products had good spatial–temporal consistency and similar magnitude(RMSD approximates 0.1). Overall, the approach provides a new operational way to estimate high spatial resolution and high temporal frequency FVC from multiple remote sensing datasets.展开更多
Quantitative precipitation estimation and rainfall monitoring based on meteorological data, potentially provides continuous, high-resolution and large-coverage data, are of high practical use: Think of hydrogeological...Quantitative precipitation estimation and rainfall monitoring based on meteorological data, potentially provides continuous, high-resolution and large-coverage data, are of high practical use: Think of hydrogeological risk management, hydroelectric power, road and tourism. Both conventional long-range radars and rain-gauges suffer from measurement errors and difficulties in precipitation estimation. For efficient monitoring operation of localized rain events of limited extension and of small basins of interest, an unrealistic extremely dense rain gauge network should be needed. Alternatively C-band or S-band meteorological long range radars are able to monitor rain fields over wide areas, however with not enough space and time resolution, and with high purchase and maintenance costs. Short-range X-band radars for rain monitoring can be a valid compromise solution between the two more common rain measurement and observation instruments. Lots of scientific efforts have already focused on radar-gauge adjustment and quantitative precipitation estimation in order to improve the radar measurement techniques. After some considerations about long range radars and gauge network, this paper presents instead some examples of how X-band mini radars can be very useful for the observation of rainfall events and how they can integrate and supplement long range radars and rain gauge networks. Three case studies are presented: A very localized and intense event, a rainfall event with high temporal and spatial variability and the employ of X-band mini radar in a mountainous region with narrow valleys. The adaptability of such radar devoted to monitor rain is demonstrated.展开更多
Real time rainfall events monitoring is very important for a large number of reasons: Civil Protection, hydrogeological risk management, hydroelectric power purposes, road and traffic regulation, and tourism. Efficien...Real time rainfall events monitoring is very important for a large number of reasons: Civil Protection, hydrogeological risk management, hydroelectric power purposes, road and traffic regulation, and tourism. Efficient monitoring operations need continuous, high-resolution and large-coverage data. To monitor and observe extreme rainfall events, often much localized over small basins of interest, and that could frequently causing flash floods, an unrealistic extremely dense rain gauge network should be needed. On the other hand, common large C-band or S-band long range radars do not provide the necessary spatial and temporal resolution. Simple short-range X-band mini weather radar can be a valid compromise solution. The present work shows how a single polarization, non-Doppler and non-coherent, simple and low cost X-band radar allowed monitoring three very intense rainfall events occurred near Turin during July 2014. The events, which caused damages and floods, are detected and monitored in real time with a sample rate of 1 minute and a radial spatial resolution of 60 m, thus allowing to describe the intensity of the precipitation on each small portion of territory. This information could be very useful if used by authorities in charge of Civil Protection in order to avoid inconvenience to people and to monitor dangerous situations.展开更多
As the key driven factor of hydrological cycles and global energy transfer processes, water vapour in the atmosphere is important for observing and understanding climatic system changes. In this study, we utilized the...As the key driven factor of hydrological cycles and global energy transfer processes, water vapour in the atmosphere is important for observing and understanding climatic system changes. In this study, we utilized the multi-dimensional Kolmogorov-Zurbenko filter (KZ filter) to assimilate a near-global high-resolution (monthly 1°?× 1°?grid) humidity climate observation database that provided consistent humidity estimates from 1973 onwards;then we examined the global humidity movements based on different temporal scales that separated out according to the average spectral features of specific humidity data. Humidity climate components were restored with KZ filters to represent the long-term trends and El Nino-like interannual movements. Movies of thermal maps based on these two climate components were used to visualize the water vapour fluctuation patterns over the Earth. Current results suggest that increases in water vapour are found over a large part of the oceans and the land of Eurasia, and the most confirmed increasing pattern is over the south part of North Atlantic and around the India subcontinent;meanwhile, the surface moisture levels over lands of south hemisphere are becoming less.展开更多
基金supported by the National Natural Science Foundation of China(11332006,11272233,and 11411130150)the National Basic Research Programm of China(2012CB720101)
文摘The spatial-temporal evolution of coherent structures （CS） is significant for turbulence control and drag re- duction. Among the CS, low and high speed streak structures show typical burst phenomena. The analysis was based on a time series of three-dimensional and three-component （3D-3C） velocity fields of the flat plate turbulent boundary layer （TBL） measured by a Tomographic and Time-resolved PIV （Tomo TRPIV） system. Using multi-resolution wavelet transform and conditional sampling method, we extracted the intrinsic topologies and found that the streak structures appear in bar-like patterns. Furthermore, we seized locations and velocity information of transient CS, and then calculated the propagation velocity of CS based on spatial-temporal cross-correlation scanning. This laid a foundation for further studies on relevant dynamics properties.
基金The research was supported by National Natural Science Foundation of China,Nos.40801144 and 41171331Knowledge Innovation Program of CAS,No.KSCX1-YW-09-01the National Key Technology R&D Program,No.2008BADA8B02.
文摘While data like HJ-1 CCD images have advantageous spatial characteristics for describing crop properties,the temporal resolution of the data is rather low,which can be easily made worse by cloud contamination.In contrast,although Moderate Resolution Imaging Spectroradiometer(MODIS)can only achieve a spatial resolution of 250 m in its normalised difference vegetation index(NDVI)product,it has a high temporal resolution,covering the Earth up to multiple times per day.To combine the high spatial resolution and high temporal resolution of different data sources,a new method(Spatial and Temporal Adaptive Vegetation index Fusion Model[STAVFM])for blending NDVI of different spatial and temporal resolutions to produce high spatialtemporal resolution NDVI datasets was developed based on Spatial and Temporal Adaptive Reflectance Fusion Model(STARFM).STAVFM defines a time window according to the temporal variation of crops,takes crop phenophase into consideration and improves the temporal weighting algorithm.The result showed that the new method can combine the temporal information of MODIS NDVI and spatial difference information of HJ-1 CCD NDVI to generate an NDVI dataset with both high spatial and high temporal resolution.An application of the generated NDVI dataset in crop biomass estimation was provided.An average absolute error of 17.2%was achieved.The estimated winter wheat biomass correlated well with observed biomass(R^(2) of 0.876).We conclude that the new dataset will improve the application of crop biomass estimation by describing the crop biomass accumulation in detail.There is potential to apply the approach in many other studies,including crop production estimation,crop growth monitoring and agricultural ecosystem carbon cycle research,which will contribute to the implementation of Digital Earth by describing land surface processes in detail.
基金Supported by the National Key Research and Development Program of China (2018YFC1506501, 2018YFA0605503, and2016YFB0501502)Special Program of Gaofen Satellites (04-Y30B01-9001-18/20-3-1)National Natural Science Foundation of China (41871230 and 41871231)。
文摘High spatial resolution and high temporal frequency fractional vegetation cover(FVC) products have been increasingly in demand to monitor and research land surface processes. This paper develops an algorithm to estimate FVC at a 30-m/15-day resolution over China by taking advantage of the spatial and temporal information from different types of sensors: the 30-m resolution sensor on the Chinese environment satellite(HJ-1) and the 1-km Moderate Resolution Imaging Spectroradiometer(MODIS). The algorithm was implemented for each main vegetation class and each land cover type over China. First, the high spatial resolution and high temporal frequency normalized difference vegetation index(NDVI) was acquired by using the continuous correction(CC) data assimilation method. Then, FVC was generated with a nonlinear pixel unmixing model. Model coefficients were obtained by statistical analysis of the MODIS NDVI. The proposed method was evaluated based on in situ FVC measurements and a global FVC product(GEOV1 FVC). Direct validation using in situ measurements at 97 sampling plots per half month in 2010 showed that the annual mean errors(MEs) of forest, cropland, and grassland were-0.025, 0.133, and 0.160, respectively, indicating that the FVCs derived from the proposed algorithm were consistent with ground measurements [R2 = 0.809,root-mean-square deviation(RMSD) = 0.065]. An intercomparison between the proposed FVC and GEOV1 FVC demonstrated that the two products had good spatial–temporal consistency and similar magnitude(RMSD approximates 0.1). Overall, the approach provides a new operational way to estimate high spatial resolution and high temporal frequency FVC from multiple remote sensing datasets.
文摘Quantitative precipitation estimation and rainfall monitoring based on meteorological data, potentially provides continuous, high-resolution and large-coverage data, are of high practical use: Think of hydrogeological risk management, hydroelectric power, road and tourism. Both conventional long-range radars and rain-gauges suffer from measurement errors and difficulties in precipitation estimation. For efficient monitoring operation of localized rain events of limited extension and of small basins of interest, an unrealistic extremely dense rain gauge network should be needed. Alternatively C-band or S-band meteorological long range radars are able to monitor rain fields over wide areas, however with not enough space and time resolution, and with high purchase and maintenance costs. Short-range X-band radars for rain monitoring can be a valid compromise solution between the two more common rain measurement and observation instruments. Lots of scientific efforts have already focused on radar-gauge adjustment and quantitative precipitation estimation in order to improve the radar measurement techniques. After some considerations about long range radars and gauge network, this paper presents instead some examples of how X-band mini radars can be very useful for the observation of rainfall events and how they can integrate and supplement long range radars and rain gauge networks. Three case studies are presented: A very localized and intense event, a rainfall event with high temporal and spatial variability and the employ of X-band mini radar in a mountainous region with narrow valleys. The adaptability of such radar devoted to monitor rain is demonstrated.
文摘Real time rainfall events monitoring is very important for a large number of reasons: Civil Protection, hydrogeological risk management, hydroelectric power purposes, road and traffic regulation, and tourism. Efficient monitoring operations need continuous, high-resolution and large-coverage data. To monitor and observe extreme rainfall events, often much localized over small basins of interest, and that could frequently causing flash floods, an unrealistic extremely dense rain gauge network should be needed. On the other hand, common large C-band or S-band long range radars do not provide the necessary spatial and temporal resolution. Simple short-range X-band mini weather radar can be a valid compromise solution. The present work shows how a single polarization, non-Doppler and non-coherent, simple and low cost X-band radar allowed monitoring three very intense rainfall events occurred near Turin during July 2014. The events, which caused damages and floods, are detected and monitored in real time with a sample rate of 1 minute and a radial spatial resolution of 60 m, thus allowing to describe the intensity of the precipitation on each small portion of territory. This information could be very useful if used by authorities in charge of Civil Protection in order to avoid inconvenience to people and to monitor dangerous situations.
文摘As the key driven factor of hydrological cycles and global energy transfer processes, water vapour in the atmosphere is important for observing and understanding climatic system changes. In this study, we utilized the multi-dimensional Kolmogorov-Zurbenko filter (KZ filter) to assimilate a near-global high-resolution (monthly 1°?× 1°?grid) humidity climate observation database that provided consistent humidity estimates from 1973 onwards;then we examined the global humidity movements based on different temporal scales that separated out according to the average spectral features of specific humidity data. Humidity climate components were restored with KZ filters to represent the long-term trends and El Nino-like interannual movements. Movies of thermal maps based on these two climate components were used to visualize the water vapour fluctuation patterns over the Earth. Current results suggest that increases in water vapour are found over a large part of the oceans and the land of Eurasia, and the most confirmed increasing pattern is over the south part of North Atlantic and around the India subcontinent;meanwhile, the surface moisture levels over lands of south hemisphere are becoming less.
