Detecting near-surface soil freeze-thaw cycles in high-altitude cold regions is important for understanding the Earth＇s surface system, but such studies are rare. In this study, we detected the spatial-temporal varia...Detecting near-surface soil freeze-thaw cycles in high-altitude cold regions is important for understanding the Earth＇s surface system, but such studies are rare. In this study, we detected the spatial-temporal variations in near-surface soil freeze-thaw cycles in the source region of the Yellow River（SRYR） during the period 2002–2011 based on data from the Advanced Microwave Scanning Radiometer for the Earth Observing System（AMSR-E）. Moreover, the trends of onset dates and durations of the soil freeze-thaw cycles under different stages were also analyzed. Results showed that the thresholds of daytime and nighttime brightness temperatures of the freeze-thaw algorithm for the SRYR were 257.59 and 261.28 K, respectively. At the spatial scale, the daily frozen surface（DFS） area and the daily surface freeze-thaw cycle surface（DFTS） area decreased by 0.08% and 0.25%, respectively, and the daily thawed surface（DTS） area increased by 0.36%. At the temporal scale, the dates of the onset of thawing and complete thawing advanced by 3.10（±1.4） and 2.46（±1.4） days, respectively; and the dates of the onset of freezing and complete freezing were delayed by 0.9（±1.4） and 1.6（±1.1） days, respectively. The duration of thawing increased by 0.72（±0.21） day/a and the duration of freezing decreased by 0.52（±0.26） day/a. In conclusion, increases in the annual minimum temperature and winter air temperature are the main factors for the advanced thawing and delayed freezing and for the increase in the duration of thawing and the decrease in the duration of freezing in the SRYR.展开更多
The dual-polarized ratio algorithm(DPR)for the retrieval of Arctic sea ice concentration from Advanced Microwave Scanning Radiometer-EOS(AMSR-E)data was improved using a contrast ratio(CR)parameter.In contrast to thre...The dual-polarized ratio algorithm(DPR)for the retrieval of Arctic sea ice concentration from Advanced Microwave Scanning Radiometer-EOS(AMSR-E)data was improved using a contrast ratio(CR)parameter.In contrast to three other algorithms(Artist Sea Ice algorithm,ASI;NASA-Team 2 algorithm,NT2;and AMSR-E Bootstrap algorithm,ABA),this algorithm does not use a series of tie-points or a priori values of brightness temperature of sea-ice constituents,such as open water and 100% sea ice.Instead,it is based on a ratio(a)of horizontally and vertically polarized sea ice emissivity at 36.5 GHz,which can be automatically determined by the CR.aexhibited a clear seasonal cycle:changing slowly during winter,rapidly at other times,and reaching a minimum during summer.The DPR was improved using a seasonala.The systematic diff erences in the Arctic sea ice area over the complete AMSR-E period(2002–2011)were-0.8% ±2.0% between the improved DPR and ASI;-1.3%±1.7% between the improved DPR and ABA;and-0.7% ±1.9% between the improved DPR and NT2.The improved DPR and ASI(or ABA)had small seasonal diff erences.The seasonal diff erences between the improved DPR and NT2 decreased,except in summer.The improved DPR identified extremely low ice concentration regions in the Pacific sector of the central Arctic(north of 83°N)around August 12,2010,which was confirmed by the Chinese National Arctic Research Expedition.A series of high-resolution MODIS images(250 m×250 m)of the Beaufort Sea during summer were used to assess the four algorithms.According to mean bias and standard deviations,the improved DPR algorithm performed equally well with the other three sea ice concentration algorithms.The improved DPR can provide reasonable sea ice concentration data,especially during summer.展开更多
Satellite SST(sea surface temperature) from the Advanced Microwave Scanning Radiometer for the Earth Observing System(AMSR-E) is compared with in situ temperature observations from Argo profiling floats over the globa...Satellite SST(sea surface temperature) from the Advanced Microwave Scanning Radiometer for the Earth Observing System(AMSR-E) is compared with in situ temperature observations from Argo profiling floats over the global oceans to evaluate the advantages of Argo NST(near-surface temperature: water temperature less than 1 m from the surface). By comparing Argo nominal surface temperature(~5 m) with its NST, a diurnal cycle caused by daytime warming and nighttime cooling was found, along with a maximum warming of 0.08±0.36°C during 14:00–15:00 local time. Further comparisons between Argo 5-m temperature/Argo NST and AMSR-E SST retrievals related to wind speed, columnar water vapor, and columnar cloud water indicate warming biases at low wind speed(<5 m/s) and columnar water vapor >28 mm during daytime. The warming tendency is more remarkable for AMSR-E SST/Argo 5-m temperature compared with AMSR-E SST/Argo NST, owing to the effect of diurnal warming. This effect of diurnal warming events should be excluded before validation for microwave SST retrievals. Both AMSR-E nighttime SST/Argo 5-m temperature and nighttime SST/Argo NST show generally good agreement, independent of wind speed and columnar water vapor. From our analysis, Argo NST data demonstrated their advantages for validation of satellite-retrieved SST.展开更多
Satellite-derived sea surface temperatures（SSTs） from the tropical rainfall measuring mission（TRMM）microwave imager（TMI） and the advanced microwave scanning radiometer for the earth observing system（AMSR-E） we...Satellite-derived sea surface temperatures（SSTs） from the tropical rainfall measuring mission（TRMM）microwave imager（TMI） and the advanced microwave scanning radiometer for the earth observing system（AMSR-E） were compared with non-pumped near-surface temperatures（NSTs） obtained from Argo profiling floats over the global oceans. Factors that might cause temperature differences were examined, including wind speed, columnar water vapor, liquid cloud water, and geographic location. The results show that both TMI and AMSR-E SSTs are highly correlated with the Argo NSTs; however, at low wind speeds, they are on average warmer than the Argo NSTs. The TMI performs slightly better than the AMSR-E at low wind speeds, whereas the TMI SST retrievals might be poorly calibrated at high wind speeds. The temperature differences indicate a warm bias of the TMI/AMSR-E when columnar water vapor is low, which can indicate that neither TMI nor AMSR-E SSTs are well calibrated at high latitudes. The SST in the Kuroshio Extension region has higher variability than in the Kuroshio region. The variability of the temperature difference between the satellite-retrieved SSTs and the Argo NSTs is lower in the Kuroshio Extension during spring. At low wind speeds, neither TMI nor AMSR-E SSTs are well calibrated, although the TMI performs better than the AMSR-E.展开更多
It has been observed that low temperature, rainfall, snowfall, frost have never occurred over the past 50 years in the southern China, and weather in this area is very complex, so the monitoring equipments are few. Op...It has been observed that low temperature, rainfall, snowfall, frost have never occurred over the past 50 years in the southern China, and weather in this area is very complex, so the monitoring equipments are few. Optical and thermal infrared remote sensing is influenced much by clouds, so the passive microwave Advanced Microwave Scanning Radiometer-Earth Observing System （AMSR-E） data are the best choice to monitor and analyze the development of disaster. In order to improve estimation accuracy, the dynamic learn- ing neural network was used to retrieve snow depth. The difference of brightness temperatures of TB18.7v and TB36.sv, TBI8.7H and TB36.sH, TB23,sv and TB89v, TBz3.8H and TB89H are made as four main input nodes and the snow depth is the only one output node of neural network. The mean and the standard deviation of retrieval errors are about 4.8 cm and 6.7 cm relative to the test data of ground measurements. The application analysis indicated that the neural network can be utilized to monitor the change of snow intensity distribution through passive microwave data in the complex weather of the southern China.展开更多
Satellite microwave emission mixed with signals from active sensors is referred to as radio- frequency interference （RFI）. RFI affects greatly the quality of data and retrieval products from space-bome microwave rad...Satellite microwave emission mixed with signals from active sensors is referred to as radio- frequency interference （RFI）. RFI affects greatly the quality of data and retrieval products from space-bome microwave radiometry. An accurate RFI detection will not only enhance geophysical retrievals over land but also provide evidence of the much-needed protection of the microwave frequency band for satellite remote sensing technologies. It is difficult to detect RFI from space-borne microwave radiometer data over winter land, because RFI signals are usually mixed with snow in mid-high latitudes. A modified principal component analysis （PCA） method is proposed in this paper for detecting microwave low frequency RFI signals. Only three original variables, one RFI index （sensitive to RFI signal） and two scattering indices （sensitive to snow scattering）, are included in the vector for principal component analysis in this modified method instead of the nine or seven RFI index original variables used in a normal PCA algorithm. The principal component with higher correlation and contribution to the original RFI index is the RFI-related principal component. In the absence of a reliable validation data set of the ＂true＂ RFI, the consistency in the identified RFI distribution obtained from this method compared to other independent methods, such as the spectral difference method, the normalized PCA method, and the double PCA method, give confidence to the RFI signals＇ identification over land. The simple and reliable modified PCA method could successfully detect RFI not only in summer but also in winter AMSR-E data.展开更多
The variation in Arctic sea ice has significant implications for climate change due to its huge influence on the global heat balance. In this study, we quantified the spatio-temporal variation of Arctic sea ice distri...The variation in Arctic sea ice has significant implications for climate change due to its huge influence on the global heat balance. In this study, we quantified the spatio-temporal variation of Arctic sea ice distribution using Advanced Microwave Scanning Radiometer(AMSR-E) sea-ice concentration data from 2003 to 2013. The results found that, over this period, the extent of sea ice reached a maximum in 2004, whereas in 2007 and 2012, the extent of summer sea ice was at a minimum. It declined continuously from 2010 to 2012, falling to its lowest level since 2003. Sea-ice extent fell continuously each summer between July and mid-September before increasing again. It decreased most rapidly in September, and the summer reduction rate was 1.35 × 10~5 km^2/yr, twice as fast as the rate between 1979 and 2006, and slightly slower than from 2002 to 2011. Area with >90% sea-ice concentration decreased by 1.32 × 10~7 km^2/yr, while locations with >50% sea-ice concentration, which were mainly covered by perennial ice, were near the North Pole, the Beaufort Sea, and the Queen Elizabeth Islands. Perennial Arctic ice decreased at a rate of 1.54 × 10~5 km^2 annually over the past 11 years.展开更多
基金supported by the National Science and Technology Support Plan of China (2015BAD07B02)
文摘Detecting near-surface soil freeze-thaw cycles in high-altitude cold regions is important for understanding the Earth＇s surface system, but such studies are rare. In this study, we detected the spatial-temporal variations in near-surface soil freeze-thaw cycles in the source region of the Yellow River（SRYR） during the period 2002–2011 based on data from the Advanced Microwave Scanning Radiometer for the Earth Observing System（AMSR-E）. Moreover, the trends of onset dates and durations of the soil freeze-thaw cycles under different stages were also analyzed. Results showed that the thresholds of daytime and nighttime brightness temperatures of the freeze-thaw algorithm for the SRYR were 257.59 and 261.28 K, respectively. At the spatial scale, the daily frozen surface（DFS） area and the daily surface freeze-thaw cycle surface（DFTS） area decreased by 0.08% and 0.25%, respectively, and the daily thawed surface（DTS） area increased by 0.36%. At the temporal scale, the dates of the onset of thawing and complete thawing advanced by 3.10（±1.4） and 2.46（±1.4） days, respectively; and the dates of the onset of freezing and complete freezing were delayed by 0.9（±1.4） and 1.6（±1.1） days, respectively. The duration of thawing increased by 0.72（±0.21） day/a and the duration of freezing decreased by 0.52（±0.26） day/a. In conclusion, increases in the annual minimum temperature and winter air temperature are the main factors for the advanced thawing and delayed freezing and for the increase in the duration of thawing and the decrease in the duration of freezing in the SRYR.
基金Supported by the National Natural Science Foundation of China(No.41406208)the Global Change Research of National Important Research Project on Science(No.2015CB953900)+1 种基金the Scientific and Youth Science Funds of Shandong Academy of Sciences,China(No.2013QN042)the Open Research Fund of the State Oceanic Administration of the People’s Republic of China Key Laboratory for Polar Science(No.3KP201203)
文摘The dual-polarized ratio algorithm(DPR)for the retrieval of Arctic sea ice concentration from Advanced Microwave Scanning Radiometer-EOS(AMSR-E)data was improved using a contrast ratio(CR)parameter.In contrast to three other algorithms(Artist Sea Ice algorithm,ASI;NASA-Team 2 algorithm,NT2;and AMSR-E Bootstrap algorithm,ABA),this algorithm does not use a series of tie-points or a priori values of brightness temperature of sea-ice constituents,such as open water and 100% sea ice.Instead,it is based on a ratio(a)of horizontally and vertically polarized sea ice emissivity at 36.5 GHz,which can be automatically determined by the CR.aexhibited a clear seasonal cycle:changing slowly during winter,rapidly at other times,and reaching a minimum during summer.The DPR was improved using a seasonala.The systematic diff erences in the Arctic sea ice area over the complete AMSR-E period(2002–2011)were-0.8% ±2.0% between the improved DPR and ASI;-1.3%±1.7% between the improved DPR and ABA;and-0.7% ±1.9% between the improved DPR and NT2.The improved DPR and ASI(or ABA)had small seasonal diff erences.The seasonal diff erences between the improved DPR and NT2 decreased,except in summer.The improved DPR identified extremely low ice concentration regions in the Pacific sector of the central Arctic(north of 83°N)around August 12,2010,which was confirmed by the Chinese National Arctic Research Expedition.A series of high-resolution MODIS images(250 m×250 m)of the Beaufort Sea during summer were used to assess the four algorithms.According to mean bias and standard deviations,the improved DPR algorithm performed equally well with the other three sea ice concentration algorithms.The improved DPR can provide reasonable sea ice concentration data,especially during summer.
基金Supported by the National Basic Research Program of China(973 Program)(No.2013CB430301)the National Natural Science Foundation of China(Nos.41321004,41206022,41406022)the National Special Research Fund for Non-Profit Marine Sector(No.201305032)
文摘Satellite SST(sea surface temperature) from the Advanced Microwave Scanning Radiometer for the Earth Observing System(AMSR-E) is compared with in situ temperature observations from Argo profiling floats over the global oceans to evaluate the advantages of Argo NST(near-surface temperature: water temperature less than 1 m from the surface). By comparing Argo nominal surface temperature(~5 m) with its NST, a diurnal cycle caused by daytime warming and nighttime cooling was found, along with a maximum warming of 0.08±0.36°C during 14:00–15:00 local time. Further comparisons between Argo 5-m temperature/Argo NST and AMSR-E SST retrievals related to wind speed, columnar water vapor, and columnar cloud water indicate warming biases at low wind speed(<5 m/s) and columnar water vapor >28 mm during daytime. The warming tendency is more remarkable for AMSR-E SST/Argo 5-m temperature compared with AMSR-E SST/Argo NST, owing to the effect of diurnal warming. This effect of diurnal warming events should be excluded before validation for microwave SST retrievals. Both AMSR-E nighttime SST/Argo 5-m temperature and nighttime SST/Argo NST show generally good agreement, independent of wind speed and columnar water vapor. From our analysis, Argo NST data demonstrated their advantages for validation of satellite-retrieved SST.
基金The National Basic Research Program(973 Program)of China under contract No.2013CB430301the National Natural Science Foundation of China under contract Nos 41440039,41206022 and 41406022the Public Science and Technology Research Funds Projects of Ocean under contract No.201305032
文摘Satellite-derived sea surface temperatures（SSTs） from the tropical rainfall measuring mission（TRMM）microwave imager（TMI） and the advanced microwave scanning radiometer for the earth observing system（AMSR-E） were compared with non-pumped near-surface temperatures（NSTs） obtained from Argo profiling floats over the global oceans. Factors that might cause temperature differences were examined, including wind speed, columnar water vapor, liquid cloud water, and geographic location. The results show that both TMI and AMSR-E SSTs are highly correlated with the Argo NSTs; however, at low wind speeds, they are on average warmer than the Argo NSTs. The TMI performs slightly better than the AMSR-E at low wind speeds, whereas the TMI SST retrievals might be poorly calibrated at high wind speeds. The temperature differences indicate a warm bias of the TMI/AMSR-E when columnar water vapor is low, which can indicate that neither TMI nor AMSR-E SSTs are well calibrated at high latitudes. The SST in the Kuroshio Extension region has higher variability than in the Kuroshio region. The variability of the temperature difference between the satellite-retrieved SSTs and the Argo NSTs is lower in the Kuroshio Extension during spring. At low wind speeds, neither TMI nor AMSR-E SSTs are well calibrated, although the TMI performs better than the AMSR-E.
基金Under the auspices of National Program on Key Basic Research Project(No.2010CB951503)National Key Technology R&D Program of China(No.2013BAC03B00)National High Technology Research and Development Program of China(No.2012AA120905)
文摘It has been observed that low temperature, rainfall, snowfall, frost have never occurred over the past 50 years in the southern China, and weather in this area is very complex, so the monitoring equipments are few. Optical and thermal infrared remote sensing is influenced much by clouds, so the passive microwave Advanced Microwave Scanning Radiometer-Earth Observing System （AMSR-E） data are the best choice to monitor and analyze the development of disaster. In order to improve estimation accuracy, the dynamic learn- ing neural network was used to retrieve snow depth. The difference of brightness temperatures of TB18.7v and TB36.sv, TBI8.7H and TB36.sH, TB23,sv and TB89v, TBz3.8H and TB89H are made as four main input nodes and the snow depth is the only one output node of neural network. The mean and the standard deviation of retrieval errors are about 4.8 cm and 6.7 cm relative to the test data of ground measurements. The application analysis indicated that the neural network can be utilized to monitor the change of snow intensity distribution through passive microwave data in the complex weather of the southern China.
文摘Satellite microwave emission mixed with signals from active sensors is referred to as radio- frequency interference （RFI）. RFI affects greatly the quality of data and retrieval products from space-bome microwave radiometry. An accurate RFI detection will not only enhance geophysical retrievals over land but also provide evidence of the much-needed protection of the microwave frequency band for satellite remote sensing technologies. It is difficult to detect RFI from space-borne microwave radiometer data over winter land, because RFI signals are usually mixed with snow in mid-high latitudes. A modified principal component analysis （PCA） method is proposed in this paper for detecting microwave low frequency RFI signals. Only three original variables, one RFI index （sensitive to RFI signal） and two scattering indices （sensitive to snow scattering）, are included in the vector for principal component analysis in this modified method instead of the nine or seven RFI index original variables used in a normal PCA algorithm. The principal component with higher correlation and contribution to the original RFI index is the RFI-related principal component. In the absence of a reliable validation data set of the ＂true＂ RFI, the consistency in the identified RFI distribution obtained from this method compared to other independent methods, such as the spectral difference method, the normalized PCA method, and the double PCA method, give confidence to the RFI signals＇ identification over land. The simple and reliable modified PCA method could successfully detect RFI not only in summer but also in winter AMSR-E data.
基金Under the auspices of National Natural Science Foundation of China(No.41676171)Qingdao National Laboratory for Marine Science and Technology of China(No.2016ASKJ02)+1 种基金Natural Science Foundation of Shandong(No.ZR2015DM015)Yantai Science&Technology Project(No.2013ZH094)
文摘The variation in Arctic sea ice has significant implications for climate change due to its huge influence on the global heat balance. In this study, we quantified the spatio-temporal variation of Arctic sea ice distribution using Advanced Microwave Scanning Radiometer(AMSR-E) sea-ice concentration data from 2003 to 2013. The results found that, over this period, the extent of sea ice reached a maximum in 2004, whereas in 2007 and 2012, the extent of summer sea ice was at a minimum. It declined continuously from 2010 to 2012, falling to its lowest level since 2003. Sea-ice extent fell continuously each summer between July and mid-September before increasing again. It decreased most rapidly in September, and the summer reduction rate was 1.35 × 10~5 km^2/yr, twice as fast as the rate between 1979 and 2006, and slightly slower than from 2002 to 2011. Area with >90% sea-ice concentration decreased by 1.32 × 10~7 km^2/yr, while locations with >50% sea-ice concentration, which were mainly covered by perennial ice, were near the North Pole, the Beaufort Sea, and the Queen Elizabeth Islands. Perennial Arctic ice decreased at a rate of 1.54 × 10~5 km^2 annually over the past 11 years.
