Successful Applications of Generic Atmospheric Correction Online Service for InSAR (GACOS) to the Reduction of Atmospheric Effects on InSAR Observations

The tremendous development of Synthetic Aperture Radar(SAR)missions in recent years facilitates the study of smaller amplitude ground deformation over greater spatial scales using longer time series.However,this poses greater challenges for correcting atmospheric effects due to the wider coverage of SAR imagery than ever.Previous attempts have used observations from Global Positioning System(GPS)and Numerical Weather Models(NWMs)to separate atmospheric delays,but they are limited by(1)The availability(and distribution)of GPS stations;(2)The low spatial resolution of NWM;And(3)The difficulties in quantifying their performance.To overcome these limitations,we have developed the Generic Atmospheric Correction Online Service for InSAR(GACOS)which utilizes the high-resolution European Centre for Medium-Range Weather Forecasts(ECMWF)products using an Iterative Tropospheric Decomposition(ITD)model.This enables the reduction of the coupling effects of the troposphere turbulence and stratification and hence achieves equivalent performances over flat and mountainous terrains.GACOS comprises a range of notable features:(1)Global coverage;(2)All-weather,all-time usability;(3)Available with a maximum of two-day latency;And(4)Indicators available to assess the model’s performance and feasibility.In this paper,we demonstrate some successful applications of the GACOS online service to a variety of geophysical studies.

Assessing Landsat-8 atmospheric correction schemes in low to moderate turbidity waters from a global perspective

Atmospheric correction is one of the major challenges in ocean color remote sensing,thus threatening comprehensive evaluation of water quality within aquatic environments.In this study,five state-of-the-art atmospheric correction(AC)processors(i.e.Acolite,C2RCC,iCOR,L2gen,and Polymer)were applied to Operational Land Imager(OLI)Landsat-8 scenes and evaluated against in situ measurements across various types of waters worldwide.A total of 262 matchups between in situ measured and satellite-derived remote sensing reflectance(R_(rs))at 20 sites were obtained between August 2013 and August 2021.Classification of optical water types(OWTs)was carried out using in situ measurements with matched satellite observations.OWT-specific analysis demonstrated that L2gen produced the most accurate Rrs with R^(2)≥0.74 and root mean squared error(RMSE)≤0.0018 sr^(–1) for the four visible bands of OLI,followed by Polymer,C2RCC,iCOR,and Acolite.In terms of R_(rs) spectral similarity,C2RCC yielded the lowest spectral angle(SA)of 8.55°,followed by L2gen(SA=9.20°).The advantage and disadvantage of each AC scheme were discussed.Recommendations to improve the accuracy for atmospheric correction were made,such as polarization observations and concurrent aerosol and ocean color measurements.

Performances of six reanalysis profile products in the atmospheric correction of passive microwave data for estimating land surface temperature under cloudy-sky conditions

Passive microwave(PMW)observations from the Advanced Microwave Scanning Radiometer 2 provide a way to obtain cloudy land surface temperatures(LSTs).However,atmospheric corrections must be performed on cloudy LSTs due to the cloud effect at higher frequencies.In this paper,six reanalyzed profiles,including the fifth-generation European Centre for Medium-range Weather Forecasts Reanalysis(ERA5),Interim Reanalysis(ERA-Interim),Japanese 55-year Reanalysis Data(JRA-55),Modern-Era Retrospective analysis for Research and Application V2(MERRA2),National Centers for Environmental Prediction(NCEP)/Final Operational Global Analysis(FNL),and NCEP/Global Forecasting System(GFS),were compared with 2829 radiosonde profiles derived from the University of Wyoming.Then,their performances in correcting the atmospheric effects of LSTs at cloudy skies were investigated.Results showed that the ERA5 had the best accuracy in revealing the actual atmospheric conditions,and the RMSEs of transmittance,downward radiance,and upward radiance were about 0.007,2.01,and 1.89 K,respectively.The RMSEs between the estimated LSTs and referenced LSTs varied from 3.15 K of the ERA5 to 6.12 K of the NCEP/FNL,indicating the ERA5 can be recommended for the atmospheric correction of PMW-based LST retrievals.Additionally,transmittance accuracy plays an essential role in impacting the LST retrievals in any weather.

Atmospheric correction for HY-1C CZI images using neural network in western Pacific region

With a spatial resolution of 50 m,a revisit time of three days,and a swath of 950 km,the coastal zone imager(CZI)offers great potential in monitoring coastal zone dynamics.Accurate atmo-spheric correction(AC)is needed to exploit the potential of quantitative ocean color inversion.However,due to the band setting of CZI,the AC over coastal waters in the western Pacific region with complex optical properties cannot be realized easily.This research introduces a novel neural network(NN)AC algorithm for CZI data over coastal waters.Total 100,000 match-ups of HY-1 C CZI-observed reflectance at the top-of-atmosphere and Operational Land Imager(OLI)-retrieved high-quality remote sensing reflectance(Rrs)at the CZI bands are built to train the NN model.These reflectance data are obtained from the standard AC algorithm in the SeaDAS.Results indicate that the distributions of the CZI retrieved Rrs were consistent with the quasi-synchronous OLI data,but the spatial information from the CZI is more detailed.Then,the accuracy of the CZI data for AC is evaluated using the multi-source in-situ data.Results further show that the NN-AC can successfully retrieve Rrs for CZI and the coefficients of determination in the blue,green,red,and near-infrared bands were 0.70,0.77,0.76,and 0.67,respectively.The NN algorithm does not depend on shortwave-infrared bands and runs very fast once properly trained.

Land Surface Temperature Change in Hangzhou Urban Area in Recent 20 Years Based on Landsat Image

Since the reform and opening-up in 1978, the urbanization level of our country has been continuously improved and the urban development has made great progress. However, with the rapid expansion of urban construction land, the population density and building density have been greatly increased, resulting in the urban heat island effect, which has negative impact on the urban thermal environment and restricts the high-quality development of urbanization. This paper focuses on how the urban surface thermal environment of Hangzhou changes in 20 years. In this paper, the characteristics of land surface temperature (LST) in Hangzhou urban area from 2000 to 2020 were studied by using Landsat images. The radiative transfer equation method is used to retrieve the land surface temperature, and the retrieval results are analyzed. The results show that: 1) the land surface temperature in Hangzhou city area has a slight upward trend in the past 20 years;2) the area of high temperature area is expanding;3) the land surface temperature in the city center area has decreased significantly in the past 20 years, while the ground temperature in other areas around the city center has increased significantly.

The influence of pressure waves in tidal gravity records

For the reduction of atmospheric effects,observed gravity has initially been corrected by using the computed barometric admittance k of the in situ measured pressure,expressed in nms-2/hPa units and estimated by least squares method.However,the local pressure changes alone cannot account for the atmospheric mass attraction and loading when the coherent pressure field exceeds a specific size,i.e.,with increasing periodicities.To overcome this difficulty,it is necessary to compute the total atmospheric effect at each station using the global pressure field.However,the direct subtraction of the total gravity effect,provided by the models of pressure correction,is not yet satisfactory for S2 and other tidal components,such as K2 and P1,which include solar heating pressure tides.This paper identifies the origin of the problem and presents strategies to obtain a satisfactory solution.First,we set up a difference vector between the tidal factors of M2 and S2 after correction of the pressure and ocean tides effects.This vector,hereafter denoted as RES,presents the advantage of being practically insensitive to calibration errors.The minimum discrepancy between the tidal parameters of M2 and S2 corresponds to the minimum of the RES vector norm d.Secondly we adopt the hybrid pressure correction method,separating the local and the global pressure contribution of the models and replacing the local contribution by the pressure measured at the station multiplied by an admittance kATM.We tested this procedure on 8 stations from the IGETS superconducting gravimeters network(former GGP network).For stations at an altitude lower than 1000 m,the value of dopt is always smaller than0.0005.The discrepancy between the tidal parameters of the M2 and S2 waves is always lower than0.05% on the amplitude factors and 0.025° on the phases.For these stations,a correlation exists between the altitude and the value kopt.The results at the three Central European stations Conrad,Pecny and Vienna are in excellent agreement(0.05%) with the DDW99NH model for all the main tidal waves.

First Look of Surface Vegetation from the Advanced Geostationary Radiation Imager(AGRI) onboard Fengyun-4B

For many years, the status of surface vegetation has been monitored by using polar-orbiting satellite imagers such as Moderate Resolution Imaging Spectroradiometer(MODIS). However, limited availability of clear-sky samples makes the derived vegetation index dependent on multiple days of observations. High-frequency observations from the geostationary Fengyun(FY) satellites can significantly reduce the influence of clouds on the synthesis of terrestrial normalized difference vegetation index(NDVI). In this study, we derived the land surface vegetation index based on observational data from the Advanced Geostationary Radiation Imager(AGRI) onboard the FY-4B geostationary satellite. First, the AGRI reflectance of visible band and near-infrared band is corrected to the land surface reflectance by the 6S radiative transfer model. The bidirectional reflectance distribution function(BRDF) model is then used to normalize the AGRI surface reflectance at different observation angles and solar geometries, and an angle-independent reflectance is derived. The AGRI surface reflectance is further corrected to the MODIS levels according to the AGRI spectral response function(SRF). Finally, the daily AGRI data are used to synthesize the surface vegetation index. It is shown that the spatial distribution of NDVI images retrieved by single-day AGRI is consistent with that of 16-day MODIS data. At the same time, the dynamic range of the revised NDVI is closer to that of MODIS.

Innovative Analysis Ready Data(ARD)product and process requirements,software system design,algorithms and implementation at the midstream as necessary-but-notsuffcient precondition of the downstream in a new notion of Space Economy 4.0-Part 1:Problem background in Artificial General Intelligence(AGI)

Aiming at the convergence between Earth observation(EO)Big Data and Artificial General Intelligence(AGI),this two-part paper identifies an innovative,but realistic EO optical sensory imagederived semantics-enriched Analysis Ready Data(ARD)productpair and process gold standard as linchpin for success of a new notion of Space Economy 4.0.To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers,it is regarded as necessarybut-not-sufficient“horizontal”(enabling)precondition for:(I)Transforming existing EO big raster-based data cubes at the midstream segment,typically affected by the so-called data-rich information-poor syndrome,into a new generation of semanticsenabled EO big raster-based numerical data and vector-based categorical(symbolic,semi-symbolic or subsymbolic)information cube management systems,eligible for semantic content-based image retrieval and semantics-enabled information/knowledge discovery.(II)Boosting the downstream segment in the development of an ever-increasing ensemble of“vertical”(deep and narrow,user-specific and domain-dependent)value–adding information products and services,suitable for a potentially huge worldwide market of institutional and private end-users of space technology.For the sake of readability,this paper consists of two parts.In the present Part 1,first,background notions in the remote sensing metascience domain are critically revised for harmonization across the multidisciplinary domain of cognitive science.In short,keyword“information”is disambiguated into the two complementary notions of quantitative/unequivocal information-as-thing and qualitative/equivocal/inherently ill-posed information-as-data-interpretation.Moreover,buzzword“artificial intelligence”is disambiguated into the two better-constrained notions of Artificial Narrow Intelligence as part-without-inheritance-of AGI.Second,based on a betterdefined and better-understood vocabulary of multidisciplinary terms,existing EO optical sensory image-derived Level 2/ARD products and processes are investigated at the Marr five levels of understanding of an information processing system.To overcome their drawbacks,an innovative,but realistic EO optical sensory image-derived semantics-enriched ARD product-pair and process gold standard is proposed in the subsequent Part 2.

Innovative Analysis Ready Data(ARD)product and process requirements,software system design,algorithms and implementation at the midstream as necessary-but-notsufficient precondition of the downstream in a new notion of Space Economy 4.0-Part 2:Software developments

Aiming at the convergence between Earth observation(EO)Big Data and Artificial General Intelligence(AGI),this paper consists of two parts.In the previous Part 1,existing EO optical sensory imagederived Level 2/Analysis Ready Data(ARD)products and processes are critically compared,to overcome their lack of harmonization/standardization/interoperability and suitability in a new notion of Space Economy 4.0.In the present Part 2,original contributions comprise,at the Marr five levels of system understanding:(1)an innovative,but realistic EO optical sensory image-derived semantics-enriched ARD co-product pair requirements specification.First,in the pursuit of third-level semantic/ontological interoperability,a novel ARD symbolic(categorical and semantic)co-product,known as Scene Classification Map(SCM),adopts an augmented Cloud versus Not-Cloud taxonomy,whose Not-Cloud class legend complies with the standard fully-nested Land Cover Classification System’s Dichotomous Phase taxonomy proposed by the United Nations Food and Agriculture Organization.Second,a novel ARD subsymbolic numerical co-product,specifically,a panchromatic or multispectral EO image whose dimensionless digital numbers are radiometrically calibrated into a physical unit of radiometric measure,ranging from top-of-atmosphere reflectance to surface reflectance and surface albedo values,in a five-stage radiometric correction sequence.(2)An original ARD process requirements specification.(3)An innovative ARD processing system design(architecture),where stepwise SCM generation and stepwise SCM-conditional EO optical image radiometric correction are alternated in sequence.(4)An original modular hierarchical hybrid(combined deductive and inductive)computer vision subsystem design,provided with feedback loops,where software solutions at the Marr two shallowest levels of system understanding,specifically,algorithm and implementation,are selected from the scientific literature,to benefit from their technology readiness level as proof of feasibility,required in addition to proven suitability.To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers,the proposed EO optical sensory image-derived semantics-enriched ARD product-pair and process reference standard is highlighted as linchpin for success of a new notion of Space Economy 4.0.

Vegetation Products Derived from Fengyun-3D Medium Resolution Spectral Imager-Ⅱ

The surface vegetation condition has been operationally monitored from space for many years by the Advanced Very High Resolution Radiometer(AVHRR) and the Moderate Resolution Imaging Spectroradiometer(MODIS) instruments. As these instruments are close to the end of their design life, the surface vegetation products are required by many users from the new satellite missions. The MEdium Resolution Spectral Imager-Ⅱ(MERSI-Ⅱ) onboard the Fengyun(FY) satellite(FY-3 series;FY-3 D) is used to retrieve surface vegetation parameters. First, MERSI-Ⅱ solar channel measurements at the red and near-infrared(NIR) bands at the top of atmosphere(TOA) are corrected to the surface reflectances at the top of canopy(TOC) by removing the contributions of scattering and absorption of molecules and aerosols. The normalized difference vegetation index(NDVI) at both the TOA and TOC is then produced by using the same algorithms as the MODIS and AVHRR. The MERSI-Ⅱ enhanced VI(EVI) at the TOC is also developed. The MODIS technique of compositing the NDVI at various timescales is applied to MERSI-Ⅱ to generate the gridded products at different resolutions. The MERSI-Ⅱ VI products are consistent with the MODIS data without systematic biases. Compared to the current MERSI-Ⅱ EVI generated from the ground operational system, the MERSI-Ⅱ EVI from this study has a much better agreement with MODIS after atmospheric correction.