In this paper,we will conclude the results of Bufeng-1(BF-1)A/B data processing,calibration workflow,and validation of the calibrated sea surface winds,land surface soil moisture,and sea surface height measurements.Si...In this paper,we will conclude the results of Bufeng-1(BF-1)A/B data processing,calibration workflow,and validation of the calibrated sea surface winds,land surface soil moisture,and sea surface height measurements.Since 2019,the BF-1 mission has operated in-orbit for over 4 years.The Earth reflected delay Doppler maps(DDMs)are continuously collected to perform global sea surface and land observations.At the same time,the intermediate frequency(IF)raw data are also obtained for 12 seconds every pass in diagnostic mode.To begin with,a brief description of the spaceborne Global Navigation Satellite System Reflectometry(GNSS-R)technique will be provided in the introduction.Next,we will present the overview of Chinese BF-1 mission and the data specifications used in our research.In the next section,the BF-1 mission-related spaceborne power calibration and validation are presented to show the support to power DDM observable production for sea surface and land surface applications.Then,the status of Chinese Beidou System(BDS)Equivalent Isotropic Radiated Power(EIRP)acquisition programme is then introduced.Furthermore,the latest sea surface height(SSH)measurements results including two modes(group delay and carrier phase)and wind speed derivation based on machine learning(ML)method will be spatial-temporal aligned and validated with auxiliary datasets including Denmark Technology University(DTU)mean sea surface(MSS)products and European Centre for Medium-Range Weather Forecasts(ECMWF)ERA5 reanalysis.The previous published results of sea surface winds retrieval under Hurricane conditions and soil moisture retrieval are also reviewed for the BF-1 mission applications.Finally,the conclusion of BF-1 derived results will be discussed to draw out ongoing/future works.展开更多
Reflected signals from global navigation satellite systems（GNSSs） have been widely acknowledged as an important remote sensing tool for retrieving sea surface wind speeds.The power of GNSS reflectometry（GNSS-R）sig...Reflected signals from global navigation satellite systems（GNSSs） have been widely acknowledged as an important remote sensing tool for retrieving sea surface wind speeds.The power of GNSS reflectometry（GNSS-R）signals can be mapped in delay chips and Doppler frequency space to generate delay Doppler power maps（DDMs）,whose characteristics are related to sea surface roughness and can be used to retrieve wind speeds.However,the bistatic radar cross section（BRCS）,which is strongly related to the sea surface roughness,is extensively used in radar.Therefore,a bistatic radar cross section（BRCS） map with a modified BRCS equation in a GNSS-R application is introduced.On the BRCS map,three observables are proposed to represent the sea surface roughness to establish a relationship with the sea surface wind speed.Airborne Hurricane Dennis（2005） GNSS-R data are then used.More than 16 000 BRCS maps are generated to establish GMFs of the three observables.Finally,the proposed model and classic one-dimensional delay waveform（DW） matching methods are compared,and the proposed model demonstrates a better performance for the high wind speed retrievals.展开更多
This paper presents the TDS-1 GNSS reflectometry wind Geophysical Model Function(GMF)response to GPS block types.The observables were extracted from Delay Doppler Maps(DDMs)after taking the receiver antenna gains effe...This paper presents the TDS-1 GNSS reflectometry wind Geophysical Model Function(GMF)response to GPS block types.The observables were extracted from Delay Doppler Maps(DDMs)after taking the receiver antenna gains effects and GNSS-R geometry effects into account.Since the DDM is affected by GPS EffectiveIsotropic Radiated Power(EIRP),we first investigate the sensitivity of observables to the GPS block.Additionally,the observables at high SNRs are more sensitive to wind speed,but the spatial coverage at high signal to noise ratios(SNRs)is lower,while DDMs at low SNRs have the opposite characteristics.To balance the accuracy and spatial coverage,the DDM datasets are divided into two parts:high SNR(>0 dB)and low SNR(>−10 dB and≤0 dB)to develop wind GMF.Then,the influences of GPS block on wind speed retrieval both at high and low SNR is analyzed.Results show that the block types have impacts on wind GMF and the use of a prior GPS block can contribute to a better wind speed retrieval both at high and low SNR.Compared with ASCAT,the Root Mean Square Error(RMSE)value of wind speed retrieval at high and low SNR are 2.19 m/s and 3.13 m/s,respectively,when all TDS data are processed without distinguishing GPS block types.However,if the TDS data are separately processed and used to develop wind GMF through different blocks,both the accuracy and correlation coefficient can be improved to some extent.Finally,the influence of significant height of the swell(Hs)on SNR observables is analyzed,and it is demonstrated that there is no obvious linear or nonlinear relationship between them.展开更多
The retrieval of sea surface wind speed is a key application of Global Navigation Satellite System-Reflectometry(GNSS-R).The continuous advancement of deep learning technologies has enabled the application of Convolut...The retrieval of sea surface wind speed is a key application of Global Navigation Satellite System-Reflectometry(GNSS-R).The continuous advancement of deep learning technologies has enabled the application of Convolutional Neural Network(CNN)models to retrieve sea surface wind speed from GNSS-R observables.However,the standard CNN models assign equal weight to all features,overlooking the more relevant ones,which reduces training efficiency and accuracy.To address this issue,this paper proposes a CNN model that incorporates the Squeeze-and-Excitation Network(SENet)attention mechanism,named CNN-SENet.The CNN-SENet model increases the weight for important features while suppressing the weight for less relevant ones,thereby improving accuracy and training efficiency.Results indicate that the CNN-SENet demonstrates a significant advantage in training efficiency over the standard CNN,reducing training time by nearly half.Additionally,the CNN-SENet model predicts the wind speeds in the range of 0-40 m/s with a Root Mean Square Error(RMSE)of 1.29 m/s and a coefficient of determination(R^(2))of 62.4%.It also outperforms both the standard CNN and the Geophysical Model Function(GMF),improving wind speed accuracy by 0.14 m/s and 0.62 m/s,respectively.Furthermore,the CNN-SENet model exhibits superior temporal generalization compared to the standard CNN.展开更多
Spaceborne global navigation satellite system-reflectometry has become an effective technique for Soil Moisture(SM)retrieval.However,the accuracy of global SM retrieval using a single model is limited due to the compl...Spaceborne global navigation satellite system-reflectometry has become an effective technique for Soil Moisture(SM)retrieval.However,the accuracy of global SM retrieval using a single model is limited due to the complexity of land surface.Introducing redundant ancillary data may also result in over-reliance problems.Therefore,we propose a method for SM retrieval that considers geographical disparities using the data from Cyclone GNSS(CYGNSS)obser-vations and Soil Moisture Active and Passive(SMAP)product.Based on the CYGNSS effective reflectivity and ancillary datasets of SMAP,we establish five models for each grid with different parameters to achieve global SM retrieval.Subsequently,an optimal model,determined by the performance indicator,is used for SM retrieval.The results show that the root mean square error SRMsE with the improved methodis decreased by 9.1%using SMAP SM as reference with the SRMsE=0.040 cm^(3)/cm^(3) compared with using single reflectivity-temperature-vegetation method.Additionally,using the in-situ SM of International Soil Moisture Network as reference,the overall correlation coeffcient R and SRMSE values with the improved method are 0.80 and 0.064 cm^(3)/cm^(3),respectively.The average R of the chosen sites is increased by 22.7%,and the average SRMse is decreased by 8.7%.The results indicate that the improved method can better retrieve SM in both global and local scales without redundant auxiliary data.展开更多
Sea ice,a significant component in polar regions,plays a crucial role in climate change through its varying conditions.In Global Navigation Satellite System-Reflectometry(GNSS-R)studies,the observed surface reflectiv...Sea ice,a significant component in polar regions,plays a crucial role in climate change through its varying conditions.In Global Navigation Satellite System-Reflectometry(GNSS-R)studies,the observed surface reflectivityΓserves as a tool to examine the physical characteristics of sea ice covers.This facilitates the large-scale estimation of first-year ice thickness using a two-layer sea ice-seawater medium model.However,it is important to note that when Sea Ice Thickness(SIT)becomes thicker,the accuracy of SIT retrieval via this two-layer model begins to decline.In this paper,we present a novel application of a spaceborne GNSS-R technique to retrieve SIT based on a three-layer model using the data from Fengyun-3E(FY-3E).Soil Moisture Ocean Salinity(SMOS)data are treated as the reference.The performance of the proposed three-layer model is evaluated against a previously established two-layer model for SIT retrieval.The analysis used the sea ice data from 2022 and 2023 with SITs less than 1.1 m.By comparing the retrieved SITs against reference values,the three-layer model achieved a Root Mean Square Error(RMSE)of 0.149 m and Correlation Coefficient(r)of 0.830,while the two-layer model reported the RMSE of 0.162 m and r value of 0.789.A scheme incorporating both models yielded superior results than either individual model,with the RMSE of 0.137 m and r reaching up to 0.852.This study is the first application of FY-3E for GNSS-R SIT retrieval,combining the advantages of a two-layer model and a three-layer model and extending the precision of GNSS-R retrieval for SIT to within 1.1 m.This provides a good reference for the future studies on GNSS-R SIT retrieval.展开更多
基金supported by the ESA&NRSCC Dragon 5 Cooperation[Grant No.58070]the National Natural Science Foundation of China[Grant No.42101409]+2 种基金China Spacesat[Grant No.SK2020014]funded by MCIN/AEI/10.13039/501100011033 with contributions by“European Union Next Generation EU/PRTR”[Grant No.RYC2019-027000-I]is also supported by Spanish National Research Council[Grant No.20215AT007].
文摘In this paper,we will conclude the results of Bufeng-1(BF-1)A/B data processing,calibration workflow,and validation of the calibrated sea surface winds,land surface soil moisture,and sea surface height measurements.Since 2019,the BF-1 mission has operated in-orbit for over 4 years.The Earth reflected delay Doppler maps(DDMs)are continuously collected to perform global sea surface and land observations.At the same time,the intermediate frequency(IF)raw data are also obtained for 12 seconds every pass in diagnostic mode.To begin with,a brief description of the spaceborne Global Navigation Satellite System Reflectometry(GNSS-R)technique will be provided in the introduction.Next,we will present the overview of Chinese BF-1 mission and the data specifications used in our research.In the next section,the BF-1 mission-related spaceborne power calibration and validation are presented to show the support to power DDM observable production for sea surface and land surface applications.Then,the status of Chinese Beidou System(BDS)Equivalent Isotropic Radiated Power(EIRP)acquisition programme is then introduced.Furthermore,the latest sea surface height(SSH)measurements results including two modes(group delay and carrier phase)and wind speed derivation based on machine learning(ML)method will be spatial-temporal aligned and validated with auxiliary datasets including Denmark Technology University(DTU)mean sea surface(MSS)products and European Centre for Medium-Range Weather Forecasts(ECMWF)ERA5 reanalysis.The previous published results of sea surface winds retrieval under Hurricane conditions and soil moisture retrieval are also reviewed for the BF-1 mission applications.Finally,the conclusion of BF-1 derived results will be discussed to draw out ongoing/future works.
基金The National Natural Science Foundation of China under contract No.41371355the Director Fund Project of Institute of Remote Sensing and Digital Earth of CAS under contract No.Y6SJ0600CX
文摘Reflected signals from global navigation satellite systems（GNSSs） have been widely acknowledged as an important remote sensing tool for retrieving sea surface wind speeds.The power of GNSS reflectometry（GNSS-R）signals can be mapped in delay chips and Doppler frequency space to generate delay Doppler power maps（DDMs）,whose characteristics are related to sea surface roughness and can be used to retrieve wind speeds.However,the bistatic radar cross section（BRCS）,which is strongly related to the sea surface roughness,is extensively used in radar.Therefore,a bistatic radar cross section（BRCS） map with a modified BRCS equation in a GNSS-R application is introduced.On the BRCS map,three observables are proposed to represent the sea surface roughness to establish a relationship with the sea surface wind speed.Airborne Hurricane Dennis（2005） GNSS-R data are then used.More than 16 000 BRCS maps are generated to establish GMFs of the three observables.Finally,the proposed model and classic one-dimensional delay waveform（DW） matching methods are compared,and the proposed model demonstrates a better performance for the high wind speed retrievals.
基金supported by the Funds for Creative Research Groups of China[Grant no.41721003]the National Natural Science Foundation of China[Grant nos.41825009 and 41774034].
文摘This paper presents the TDS-1 GNSS reflectometry wind Geophysical Model Function(GMF)response to GPS block types.The observables were extracted from Delay Doppler Maps(DDMs)after taking the receiver antenna gains effects and GNSS-R geometry effects into account.Since the DDM is affected by GPS EffectiveIsotropic Radiated Power(EIRP),we first investigate the sensitivity of observables to the GPS block.Additionally,the observables at high SNRs are more sensitive to wind speed,but the spatial coverage at high signal to noise ratios(SNRs)is lower,while DDMs at low SNRs have the opposite characteristics.To balance the accuracy and spatial coverage,the DDM datasets are divided into two parts:high SNR(>0 dB)and low SNR(>−10 dB and≤0 dB)to develop wind GMF.Then,the influences of GPS block on wind speed retrieval both at high and low SNR is analyzed.Results show that the block types have impacts on wind GMF and the use of a prior GPS block can contribute to a better wind speed retrieval both at high and low SNR.Compared with ASCAT,the Root Mean Square Error(RMSE)value of wind speed retrieval at high and low SNR are 2.19 m/s and 3.13 m/s,respectively,when all TDS data are processed without distinguishing GPS block types.However,if the TDS data are separately processed and used to develop wind GMF through different blocks,both the accuracy and correlation coefficient can be improved to some extent.Finally,the influence of significant height of the swell(Hs)on SNR observables is analyzed,and it is demonstrated that there is no obvious linear or nonlinear relationship between them.
基金supported by the National Key R&D Program of China(2021YFB3901301)the National Natural Science Foundation of China(42271420)+1 种基金the Natural Science Foundation for Young Scholars of Jiangsu Province,China(BK20220366)Jiangsu Province Department of Natural Resources Science and Technology Innovation Project(JSZRKJ202406).
文摘The retrieval of sea surface wind speed is a key application of Global Navigation Satellite System-Reflectometry(GNSS-R).The continuous advancement of deep learning technologies has enabled the application of Convolutional Neural Network(CNN)models to retrieve sea surface wind speed from GNSS-R observables.However,the standard CNN models assign equal weight to all features,overlooking the more relevant ones,which reduces training efficiency and accuracy.To address this issue,this paper proposes a CNN model that incorporates the Squeeze-and-Excitation Network(SENet)attention mechanism,named CNN-SENet.The CNN-SENet model increases the weight for important features while suppressing the weight for less relevant ones,thereby improving accuracy and training efficiency.Results indicate that the CNN-SENet demonstrates a significant advantage in training efficiency over the standard CNN,reducing training time by nearly half.Additionally,the CNN-SENet model predicts the wind speeds in the range of 0-40 m/s with a Root Mean Square Error(RMSE)of 1.29 m/s and a coefficient of determination(R^(2))of 62.4%.It also outperforms both the standard CNN and the Geophysical Model Function(GMF),improving wind speed accuracy by 0.14 m/s and 0.62 m/s,respectively.Furthermore,the CNN-SENet model exhibits superior temporal generalization compared to the standard CNN.
基金supported by Natural Science and Technology Planning Foundation of Guangxi (guikeAD23026257)the National Natural Science Foundation of China (42064002 and 42074029)and the“Ba Gui Scholars”program of the provincial government of Guangxi。
文摘Spaceborne global navigation satellite system-reflectometry has become an effective technique for Soil Moisture(SM)retrieval.However,the accuracy of global SM retrieval using a single model is limited due to the complexity of land surface.Introducing redundant ancillary data may also result in over-reliance problems.Therefore,we propose a method for SM retrieval that considers geographical disparities using the data from Cyclone GNSS(CYGNSS)obser-vations and Soil Moisture Active and Passive(SMAP)product.Based on the CYGNSS effective reflectivity and ancillary datasets of SMAP,we establish five models for each grid with different parameters to achieve global SM retrieval.Subsequently,an optimal model,determined by the performance indicator,is used for SM retrieval.The results show that the root mean square error SRMsE with the improved methodis decreased by 9.1%using SMAP SM as reference with the SRMsE=0.040 cm^(3)/cm^(3) compared with using single reflectivity-temperature-vegetation method.Additionally,using the in-situ SM of International Soil Moisture Network as reference,the overall correlation coeffcient R and SRMSE values with the improved method are 0.80 and 0.064 cm^(3)/cm^(3),respectively.The average R of the chosen sites is increased by 22.7%,and the average SRMse is decreased by 8.7%.The results indicate that the improved method can better retrieve SM in both global and local scales without redundant auxiliary data.
基金funded by the National Natural Science Foundation of China under Grant 42001362.
文摘Sea ice,a significant component in polar regions,plays a crucial role in climate change through its varying conditions.In Global Navigation Satellite System-Reflectometry(GNSS-R)studies,the observed surface reflectivityΓserves as a tool to examine the physical characteristics of sea ice covers.This facilitates the large-scale estimation of first-year ice thickness using a two-layer sea ice-seawater medium model.However,it is important to note that when Sea Ice Thickness(SIT)becomes thicker,the accuracy of SIT retrieval via this two-layer model begins to decline.In this paper,we present a novel application of a spaceborne GNSS-R technique to retrieve SIT based on a three-layer model using the data from Fengyun-3E(FY-3E).Soil Moisture Ocean Salinity(SMOS)data are treated as the reference.The performance of the proposed three-layer model is evaluated against a previously established two-layer model for SIT retrieval.The analysis used the sea ice data from 2022 and 2023 with SITs less than 1.1 m.By comparing the retrieved SITs against reference values,the three-layer model achieved a Root Mean Square Error(RMSE)of 0.149 m and Correlation Coefficient(r)of 0.830,while the two-layer model reported the RMSE of 0.162 m and r value of 0.789.A scheme incorporating both models yielded superior results than either individual model,with the RMSE of 0.137 m and r reaching up to 0.852.This study is the first application of FY-3E for GNSS-R SIT retrieval,combining the advantages of a two-layer model and a three-layer model and extending the precision of GNSS-R retrieval for SIT to within 1.1 m.This provides a good reference for the future studies on GNSS-R SIT retrieval.
