查看更多>>摘要：Recent research shows that China is experiencing significant greening,with afforestation initiatives being the main cause.Quantitative calculation of vegetation change influencing factors and evaluation of the contribution of afforestation to vegetation greening in China are critical to coping with climate change and improving the implementation and efficacy of forestry projects.We investigated the temporal and spatial dynamics of the Normalized Difference Vegetation Index(NDVI)from 1982 to 2020,and quantified the contribution of afforestation initiatives,a typical human activity,to the dynamic changes of vegetation.The results showed that NDVI in China has primarily increased in the last 39 years.57％of the pixels increased,27％were stable and unchanged,and 16％decreased.Climate change was responsible for 72.34％of vegetation restoration,while human activities were responsible for 27.36％of vegetation restoration,according to residual analysis.In the future,only 14％of the regions showed continuous growth of the NDVI,while the remaining regions showed obvious antipersistence(59％will go from increasing to decreasing,and 22％will go from decreasing to increasing).The contribution of climate factors to vegetation change will decrease in the future,and human activities will become more complex.Except for Huaihe River and Taihu Lake(SPHRTL),other forestry projects showed an increasing trend of NDVI after the implementation of ecological engineering.However,due to differences in climate conditions and ecological engineering implementation,there are differences in the benefits of forestry projects.Some forestry project areas still have obvious vegetation degradation,and appropriate forestry management is necessary.This work provides a quantitative analysis of vegetation change and its driving factors in China,which will help to cope with future climate change and provide a reference for the implementation and management of ecological projects.
查看更多>>摘要：Mountains are important suppliers of freshwater to downstream areas,affecting large populations in particular in High Mountain Asia(HMA).Yet,the propagation of water from HMA headwaters to downstream areas is not fully understood,as interactions in the mountain water cycle between the cryo-,hydro-and biosphere remain elusive.We review the definition of blue and green water fluxes as liquid water that contributes to runoff at the outlet of the selected domain(blue)and water lost to the atmosphere through vapor fluxes,that is evaporation from water,ground,and interception plus transpiration(green)and propose to add the term white water to account for the(often neglected)evaporation and sublimation from snow and ice.We provide an assessment of models that can simulate the cryo-hydro-biosphere continuum and the interactions between spheres in high mountain catchments,going beyond disciplinary separations.Land surface models are uniquely able to account for such complexity,since they solve the coupled fluxes of water,energy,and carbon between the land surface and atmosphere.Due to the mechanistic nature of such models,specific variables can be compared systematically to independent remote sensing observations-providing vital insights into model accuracy and enabling the understanding of the complex watersheds of HMA.We discuss recent developments in spaceborne earth observation products that have the potential to support catchment modeling in high mountain regions.We then present a pilot study application of the mechanistic land surface model Tethys & Chloris to a glacierized watershed in the Nepalese Himalayas and discuss the use of high-resolution earth observation data to constrain the meteorological forcing uncertainty and validate model results.We use these insights to highlight the remaining challenges and future opportunities that remote sensing data presents for land surface modeling in HMA.
查看更多>>摘要：Synectic Aperture Radar(SAR)backscatter coefficient is sensitive to glacier surface physical characteristic changes,including the states of melting and refreezing,but it is also sensitive to incidence angle variation.This study explores the capability of monitoring Greenland Ice Sheet(GrIS)melting status with Sentinel-1 dual-polarized images by referring to Automatic Weather Station(AWS)records.Sentinel-1 SAR images at five coastal regions of the GrIS are obtained from 2017 to 2021.The backscatter coefficients are normalized to an incidence angle of 30° with an empirical model.Time series of five backscatter coefficients profiles covering AWS illustrates different patterns of the ice surface dielectric constant dynamics in different eleva-tions.The wet snow radar zone shows clear backscatter coefficients decreasing during the melting seasons,but the bare ice radar zone behaves more complexly during the melting seasons.The numbers of melting days at different elevations are also derived for each profile based on-3 dB backscatter coefficient decrease of HH and/or HV polarization,showing the heterogeneous ablation processes over the GrIS.The daily maximum 2 m air temperature on two consecutive days(before and on the SAR acquisition day)exceeds 0℃,and the daily average 2 m air temperature exceeds-0.5℃ on the SAR acquisition day that was recorded by the AWS finds good agreements with the-3 dB decrease of the backscatter coefficients,suggesting the GrIS surface melting can be well captured by dual-polarized Sentinel-1 C-band SAR images.The overall agreement and Kappa coefficients are mostly better than 0.85 and 0.70,respectively,for HH images and 0.80 and 0.60,respectively,for HV images,suggesting a better performance of the co-polarized image.High temporal resolution and wide-swath SAR sequence imagery provide suitable data sources for monitoring glacier surface melting-refreez-ing stats;further analysis is requested to quantitatively link the volume of melting with back-scatter coefficient and other SAR data sources.
查看更多>>摘要：The two vegetation transfer parameters of τ(Vegetation Optical Depth,VOD)and ω(Omega)could vary significantly across microwave channels in terms of frequencies,polarizations,and incidence angles,and their channel-dependent characteristics have not yet been fully investi-gated.In this study,we investigate the channel dependence of vegetation effects on micro-wave emissions from soils using a higher-order vegetation radiative transfer model of Tor Vergata.Corn was selected as the subject of investigation,and a corn growth model was developed utilizing field data collected from the multifrequency and multi-angular ground-based microwave radiation experiment from the Soil Moisture Experiment in the Luan River(SMELR).Upon compilation of the simulation dataset of microwave emissions of the corn field,the effective scattering albedo across different channels were calculated using the Tor Vergata model.Results show that vertical polarization of the vegetation optical depth is more affected by incidence angle changes,while horizontal polarization exhibits lower variations in vegeta-tion optical depth due to incidence angle adjustments.The channel dependence of vegetation optical depth can be described as the polarization dependence parameter(Cp)and the frequency dependence parameter(Cf).These two parameters enable the calculation of vegeta-tion optical depth at any channel under three adjacent frequencies(L-band,C-band and X-band).The effective scattering albedo of vegetation does not vary significantly with vegeta-tion height or angle.It primarily depends on frequency and polarization,showing an overall increasing trend with increasing frequency.The effective scattering albedo with vertical polarization is slightly higher than that with horizontal polarization at higher frequencies,while both are lower in the L-band.This investigation is helpful for understanding the vegeta-tion effects on microwave emissions from soils,ultimately advancing the accuracy of large-scale soil moisture retrieval in vegetated areas.
查看更多>>摘要：Mountain glaciers are sensitive to climate variability and can be of great importance for downstream populations due to their hydrological significance.Synthetic Aperture Radar(SAR)images are often used to monitor the characteristics of glaciers based on the back-scattering coefficient.However,the influence of satellite orbit and polarization when collecting images for wide regions has not been well considered.This study focuses on the extraction of wet snow in summer and firn in winter in West Kunlun Shan and the Tibet Interior Mountains by using Sentinel-1 C-band SAR data.The investigated regions have different climate patterns.We compare backscatter coefficient distributions for wet snow and firn,derived from maximum likelihood classification under various polarizations,alongside their respective ratios and show that polarization has a minor impact on the identification and monitoring of both wet snow and firn.However,a comparison of the wet snow ratios at different satellite orbits reveals notable differences between ascending and descending orbits in summer.We furthermore show,by analyzing weather stations on glaciers,that such effect can be related to the different acquisition time and different temperatures in the morning and afternoon and therefore to the orbit.In contrast,firn ratios across different orbits show less variation in winter,and the monitoring results consistently align with the patterns of ablation and accumulation typical under both climatic influences.These findings demonstrate glaciers'sensitivity to temperature fluctuations and the radar wave's responsiveness to surface characteristics.Consequently,when employing SAR for glacier monitoring,it is crucial to consider the influence of orbit and polarization,in combination with temperature variations,and whether the season is winter or summer.
查看更多>>摘要：Polarimetric calibration is essential for the pre-processing of Polarimetric Synthetic Aperture Radar(PolSAR)data because it effectively mitigates polarimetric distortions in the measured PolSAR data.Traditional methods of polarimetric calibration employ man-made calibrators that offer high accuracy.However,the frequency of calibration is often limited due to the labor-intensive and time-consuming nature of deploying such calibrators.Some polarimetric calibra-tion methods based on distributed targets in nature enable more frequent calibration.Nevertheless,these methods are constrained by the availability of specific distributed targets with known polarimetric properties for estimating parameters related to co-polarization chan-nel imbalance(co-pol-imba)parameters.If distributed targets are not appropriately selected or suitable targets are absent within the image scene,the accuracy of calibration will be com-promised.To address this challenge,this paper introduces the idea of cross-calibration,which uses calibrated PoISAR data to determine the real polarimetric property of distributed targets and cross-estimates co-pol-imba parameters for uncalibrated PolSAR data.Furthermore,con-sidering the disparities in imaging geometry between calibrated and uncalibrated PolSAR data,the Cross-Co-Polarization Ratio(CCPR)coefficients are proposed to select stable distributed targets in both the calibrated PolSAR data and the uncalibrated PoISAR data.This approach not only takes into account the impact of co-pol-imba on feature extraction but can also be applied in a broader range of scenes.The real data experiments on the Uninhabited Aerial Vehicle Synthetic Aperture Radar(UAVSAR)data show that the estimated results of the cross-calibration method for co-pol-imba are comparable to the polarimetric calibration method based on man-made calibrators,with an amplitude difference of 0.17 dB and a phase difference of 0.69°.
查看更多>>摘要：Due to the small size,variety,and high degree of mixing of herbaceous vegetation,remote sensing-based identification of grassland types primarily focuses on extracting major grassland categories,lacking detailed depiction.This limitation significantly hampers the development of effective evaluation and fine supervision for the rational utilization of grassland resources.To address this issue,this study concentrates on the representative grassland of Zhenglan Banner in Inner Mongolia as the study area.It integrates the strengths of Sentinel-1 and Sentinel-2 active-passive synergistic observations and introduces innovative object-oriented techniques for grassland type classification,thereby enhancing the accuracy and refinement of grassland classification.The results demonstrate the following:(1)To meet the supervision requirements of grassland resources,we propose a grassland type classification system based on remote sensing and the vegetation-habitat classification method,specifically applicable to natural grasslands in northern China.(2)By utilizing the high-spatial-resolution Normalized Difference Vegetation Index(NDVI)synthesized through the Spatial and Temporal Non-Local Filter-based Fusion Model(STNLFFM),we are able to capture the NDVI time profiles of grass-land types,accurately extract vegetation phenological information within the year,and further enhance the temporal resolution.(3)The integration of multi-seasonal spectral,polarization,and phenological characteristics significantly improves the classification accuracy of grassland types.The overall accuracy reaches 82.61％,with a kappa coefficient of 0.79.Compared to using only multi-seasonal spectral features,the accuracy and kappa coefficient have improved by 15.94％and 0.19,respectively.Notably,the accuracy improvement of the gently sloping steppe is the highest,exceeding 38％.(4)Sandy grassland is the most widespread in the study area,and the growth season of grassland vegetation mainly occurs from May to September.The sandy meadow exhibits a longer growing season compared with typical grassland and mea-dow,and the distinct differences in phenological characteristics contribute to the accurate identification of various grassland types.
查看更多>>摘要：Accurate estimation of forest terrain and canopy height is crucial for timely understanding of forest growth.Gao Fen-7(GF-7)Satellite is China's first sub-meter-level three-dimensional(3D)mapping satellite for civilian use,which was equipped with a two-line-array stereo mapping camera and a laser altimeter system that can provide stereo images and full waveform LiDAR data simultaneously.Most of the existing studies have concentrated on evaluating the accu-racy of GF-7 for topographic survey in bare land,but few have in-depth studied its ability to measure forest terrain elevation and canopy height.The purpose of this study is to evaluate the potential of GF-7 LiDAR and stereo image for forest terrain and height measurement.The Airborne Laser Scanning(ALS)data were utilized to generate reference terrain and forest vertical information.The validation test was conducted in Pu'er City,Yunnan Province of China,and encouraging results have obtained.The GF-7 LiDAR data obtained the accuracy of forest terrain elevation with RMSE of 8.01 m when 21 available laser footprints were used for results verification;meanwhile,when it was used to calculate the forest height,R2 of 0.84 and RMSE of 3.2 m were obtained although only seven effective footprints were used for result verification.The canopy height values obtained from GF-7 stereo images have also been proven to have high accuracy with the resolution of 20 m x 20 m compared with ALS data(R2=0.88,RMSE=2.98 m).When the results were verified at the forest sub-compartment scale that taking into account the forest types,further higher accuracy(R2=0.96,RMSE=1.23 m)was obtained.These results show that GF-7 has considerable application potential in forest resources monitoring.
查看更多>>摘要：This paper contrasts predicted X-band sea surface backscattering from slick-free and oil-covered sea surfaces with actual measurements acquired by the X-band satellite TerraSAR-X and COSMO-SkyMed Synthetic Aperture Radar(SAR)missions.Two SAR scenes were acquired with a temporal difference of about 36 minutes,under similar met-ocean conditions,during the North Sea's Gannet Alpha oil spill accident.The normalized radar cross section of the slick-free sea surface is predicted using the Advanced Integral Equation Model(AIEM)while the backscatter from the oiled sea surface is predicted by the AIEM augmented with the Model of Local Balance(MLB)to include the damping effect of oil slicks.Experimental results show that X-band co-polarized numerical pre-dictions agree reasonably well with both TSX and CSK actual measurements collected over slick-free sea surfaces.When dealing with oil-covered sea surfaces,the predicted backscattering reasonably agrees with TSX measurements,while it overestimates the CSK ones.This is likely due to the different spreading conditions of the oil imaged by the two satellite missions.
查看更多>>摘要：Remote sensing(RS)technologies are extensively exploited by scientists and a vast audience of local authorities,urban managers,and city planners.Coastal regions,geohazard-prone areas,and highly populated cities represent natural laboratories to apply RS technologies and test new methods.Over the last decades,many efforts have been spent on improving Earth's surface monitoring,including intensifying Earth Observation(EO)operations by the major national space agencies.They oversee to plan and make operational constellations of satellite sensors providing the scientific community with extensive research and development oppor-tunities in the geoscience field.For instance,within this framework,the European Space Agency(ESA)and the Ministry of Science and Technology of China(MOST)have sponsored,since the early 2000s,the DRAGON initiative jointly carried out by the European and Chinese RS scientific communities.This manuscript aims to provide a synthetic overview of some research activities and new methods recently designed and applied and trace the route for further developments.The main findings are related to ⅰ)the analysis of flood risk in China,ⅱ)the potential of new methods for the estimation and removal of ground displacement biases in small-baseline oriented interferometric Synthetic Aperture Radar(SAR)methods,ⅲ)the analy-sis of the inundation risk in low-lying regions using coherent and incoherent SAR methods;andⅳ)the use of SAR-based technologies for marine applications.
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