Most airborne LiDAR point cloud filter algorithms are low-precision, ineffective and low-robust in mountain region. In order to improve the precision, efficiency and robustness in this region, a normalization CSF- modified algorithm presented in this paper based on CSF (Cloth Simulation Filtering). This algorithm has high precision and robustness in a wide range of complex scenes. In the first place, the pretreatment of point cloud reject gross error. Then, establish a grid index by grid and use the lowest point of each grid mesh surface equation. Thirdly, calculate the distance between raw point cloud and fitting surface, getting normalized point cloud. Finally use CSF algorithm to simulate filtering process, getting the final shape of cloth and filtering result obtained by the shape of cloth and limit of threshold. Use a big campus area to verify the algorithm, the result shows that the algorithm can effectively correct the top information of mountains removed by CSF algorithm and improve the accuracy and robustness of point cloud filtering.
{"title":"Improvement of CSF based on a wide range of urban complex scenes","authors":"Yamin Li, Qi Chen, Chaokui Li, Pu Bu","doi":"10.1117/12.2324747","DOIUrl":"https://doi.org/10.1117/12.2324747","url":null,"abstract":"Most airborne LiDAR point cloud filter algorithms are low-precision, ineffective and low-robust in mountain region. In order to improve the precision, efficiency and robustness in this region, a normalization CSF- modified algorithm presented in this paper based on CSF (Cloth Simulation Filtering). This algorithm has high precision and robustness in a wide range of complex scenes. In the first place, the pretreatment of point cloud reject gross error. Then, establish a grid index by grid and use the lowest point of each grid mesh surface equation. Thirdly, calculate the distance between raw point cloud and fitting surface, getting normalized point cloud. Finally use CSF algorithm to simulate filtering process, getting the final shape of cloth and filtering result obtained by the shape of cloth and limit of threshold. Use a big campus area to verify the algorithm, the result shows that the algorithm can effectively correct the top information of mountains removed by CSF algorithm and improve the accuracy and robustness of point cloud filtering.","PeriodicalId":370971,"journal":{"name":"Asia-Pacific Remote Sensing","volume":"228 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122356712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Cao, S. Błoński, Wenhui Wang, S. Uprety, X. Shao, Jason Choi, E. Lynch, S. Kalluri
The NOAA-20 (aka JPSS-1) satellite was successfully launched on November 18, 2017 as the first in the JPSS satellite series, and a follow-on to the Suomi NPP satellite mission. The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard is a major Earth observing instrument with 22 spectral bands covering the 0.41um to 12.5 μm spectral range with spatial resolutions of 375 and 750 meters for the imaging and radiometric bands respectively. Since the VIIRS nadir and cryocooler doors were opened on December 13, 2017, and January 3, 2018 respectively, the instrument has been performing well and producing high quality data. The VIIRS Sensor Data Record (SDR) team has been supporting the postlaunch tests and extensive calibration/validation to ensure radiometric, spectral, and spatial performance. This paper provides a comprehensive summary of the studies in the postlaunch calibration/validation activities which enables the VIIRS SDR to reach beta, provisional, and calibrated/validation product maturity. The instrument performance is quantified through a large number of tests involving onboard, maneuvers, as well as vicarious calibration/validation. Several issues found in the ground processing are addressed through updating the calibration input parameters known as the lookup tables (LUTs). Instrument performance waivers including the non-standard aggregation mode for the Day/Night band (DNB) and related features are addressed. On-orbit anomalies and mitigations such as the longwave infrared band degradation and saturation in some bands are also discussed. With a local equator crossing time of ~1:30pm with ~50.5 min separation from Suomi NPP achieved since January 2, 2018, NOAA-20 VIIRS provides important Earth observations for generating more than 26 global environmental data records including clouds, sea surface temperature, polar wind, aerosol, vegetation fraction, ocean color, fire, snow and ice for weather, and other environmental applications.
{"title":"NOAA-20 VIIRS on-orbit performance, data quality, and operational Cal/Val support","authors":"C. Cao, S. Błoński, Wenhui Wang, S. Uprety, X. Shao, Jason Choi, E. Lynch, S. Kalluri","doi":"10.1117/12.2324329","DOIUrl":"https://doi.org/10.1117/12.2324329","url":null,"abstract":"The NOAA-20 (aka JPSS-1) satellite was successfully launched on November 18, 2017 as the first in the JPSS satellite series, and a follow-on to the Suomi NPP satellite mission. The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard is a major Earth observing instrument with 22 spectral bands covering the 0.41um to 12.5 μm spectral range with spatial resolutions of 375 and 750 meters for the imaging and radiometric bands respectively. Since the VIIRS nadir and cryocooler doors were opened on December 13, 2017, and January 3, 2018 respectively, the instrument has been performing well and producing high quality data. The VIIRS Sensor Data Record (SDR) team has been supporting the postlaunch tests and extensive calibration/validation to ensure radiometric, spectral, and spatial performance. This paper provides a comprehensive summary of the studies in the postlaunch calibration/validation activities which enables the VIIRS SDR to reach beta, provisional, and calibrated/validation product maturity. The instrument performance is quantified through a large number of tests involving onboard, maneuvers, as well as vicarious calibration/validation. Several issues found in the ground processing are addressed through updating the calibration input parameters known as the lookup tables (LUTs). Instrument performance waivers including the non-standard aggregation mode for the Day/Night band (DNB) and related features are addressed. On-orbit anomalies and mitigations such as the longwave infrared band degradation and saturation in some bands are also discussed. With a local equator crossing time of ~1:30pm with ~50.5 min separation from Suomi NPP achieved since January 2, 2018, NOAA-20 VIIRS provides important Earth observations for generating more than 26 global environmental data records including clouds, sea surface temperature, polar wind, aerosol, vegetation fraction, ocean color, fire, snow and ice for weather, and other environmental applications.","PeriodicalId":370971,"journal":{"name":"Asia-Pacific Remote Sensing","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123992187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Supraglacial debris essentially hamper the mapping of ice glaciers by remote sensing data. A semi-automatic approach for the mapping of debris covered glaciers in Astor Basin was applied, which combines the inputs from optical satellite data and the digital elevation model (DEM) data. Strong and effective pixel-based band ratios have turned out to be precise for naturally outlining clean glacier ice, however such classifications algorithm exhibit limitations in delineating debriscovered ice because of its spectral resemblance with adjacent landscape. Object based image analysis (OBIA) has risen as another examination strategy inside remote sensing. It gives a system to filter out worthless details and integrate other parts of detail into a single object, although it is also allowing contextual, shape, textural and, hierarchical principles to be used to classify imagery. Supraglacial debris-covered, snow covered glaciers and glaciated ice in Astor Basin were mapped by using Landsat 7,8 imageries gained from 2010 to 2017 and a digital elevation model (DEM) acquired from Advanced Land Observing Satellite (ALOS).The methods offered recognized their usefulness using freely accessible reasonable resolution Landsat OLI and ALOS data. Yet, the increasing availability of high resolution imageries, improved quality and the latest digital terrain data grip the potential of enhanced image segmentation and classification from OBIA approaches.
{"title":"Mapping of debris-covered glaciers in Astor basin: an object-based image analysis approach","authors":"F. Ahmad, M. Baig","doi":"10.1117/12.2324407","DOIUrl":"https://doi.org/10.1117/12.2324407","url":null,"abstract":"Supraglacial debris essentially hamper the mapping of ice glaciers by remote sensing data. A semi-automatic approach for the mapping of debris covered glaciers in Astor Basin was applied, which combines the inputs from optical satellite data and the digital elevation model (DEM) data. Strong and effective pixel-based band ratios have turned out to be precise for naturally outlining clean glacier ice, however such classifications algorithm exhibit limitations in delineating debriscovered ice because of its spectral resemblance with adjacent landscape. Object based image analysis (OBIA) has risen as another examination strategy inside remote sensing. It gives a system to filter out worthless details and integrate other parts of detail into a single object, although it is also allowing contextual, shape, textural and, hierarchical principles to be used to classify imagery. Supraglacial debris-covered, snow covered glaciers and glaciated ice in Astor Basin were mapped by using Landsat 7,8 imageries gained from 2010 to 2017 and a digital elevation model (DEM) acquired from Advanced Land Observing Satellite (ALOS).The methods offered recognized their usefulness using freely accessible reasonable resolution Landsat OLI and ALOS data. Yet, the increasing availability of high resolution imageries, improved quality and the latest digital terrain data grip the potential of enhanced image segmentation and classification from OBIA approaches.","PeriodicalId":370971,"journal":{"name":"Asia-Pacific Remote Sensing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115210714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Satellite ocean-color project offices routinely generate Level 2 and Level 3 daily Photo-synthetically Available Radiation (PAR) products. Accuracy is currently evaluated against in-situ measurements from buoys and fixed platforms at a few locations, but specifying algorithm (and other) uncertainties on a pixel-by-pixel basis is needed to assess product quality. Expressing uncertainties requires modeling the measurement, identifying all possible error sources (e.g., noise in the input variables, imperfect/incomplete mathematical model), and determining the combined uncertainty. In the present study, algorithm uncertainties associated with PAR products are considered, i.e., those due to model approximations and parameter errors (e.g., decoupling effects of clouds and clear atmosphere, neglecting diurnal variability of clouds, using aerosol climatology) assuming that the input variables (TOA reflectance at wavelengths in the PAR spectral range) are known perfectly. A procedure is provided to estimate and provide, for each pixel of a product, this uncertainty component of the total uncertainty budget, which is expected to dominate. The bias and standard deviation of the daily PAR estimates are calculated as a function of clear sky PAR and cloud factor (i.e., the effect of clouds on daily PAR). The uncertainty characterization is accomplished using an extended simulation dataset covering the 2003–2012 time period using hourly MERRA-2 input data. The large number of data points allows one to sample well atmospheric variability and in particular many variations of daytime cloudiness, for all latitudes. Selected maps of global daily and monthly PAR and associated uncertainties (bias, standard deviation), obtained from MERIS data, are analyzed. Comparisons with match-up data at the COVE calibration/evaluation site reveal that experimental uncertainties are similar to the theoretical uncertainties obtained from simulated data.
{"title":"Specifying algorithm uncertainties in satellite-derived PAR products","authors":"R. Frouin, D. Ramon, D. Jolivet, M. Compiègne","doi":"10.1117/12.2501681","DOIUrl":"https://doi.org/10.1117/12.2501681","url":null,"abstract":"Satellite ocean-color project offices routinely generate Level 2 and Level 3 daily Photo-synthetically Available Radiation (PAR) products. Accuracy is currently evaluated against in-situ measurements from buoys and fixed platforms at a few locations, but specifying algorithm (and other) uncertainties on a pixel-by-pixel basis is needed to assess product quality. Expressing uncertainties requires modeling the measurement, identifying all possible error sources (e.g., noise in the input variables, imperfect/incomplete mathematical model), and determining the combined uncertainty. In the present study, algorithm uncertainties associated with PAR products are considered, i.e., those due to model approximations and parameter errors (e.g., decoupling effects of clouds and clear atmosphere, neglecting diurnal variability of clouds, using aerosol climatology) assuming that the input variables (TOA reflectance at wavelengths in the PAR spectral range) are known perfectly. A procedure is provided to estimate and provide, for each pixel of a product, this uncertainty component of the total uncertainty budget, which is expected to dominate. The bias and standard deviation of the daily PAR estimates are calculated as a function of clear sky PAR and cloud factor (i.e., the effect of clouds on daily PAR). The uncertainty characterization is accomplished using an extended simulation dataset covering the 2003–2012 time period using hourly MERRA-2 input data. The large number of data points allows one to sample well atmospheric variability and in particular many variations of daytime cloudiness, for all latitudes. Selected maps of global daily and monthly PAR and associated uncertainties (bias, standard deviation), obtained from MERIS data, are analyzed. Comparisons with match-up data at the COVE calibration/evaluation site reveal that experimental uncertainties are similar to the theoretical uncertainties obtained from simulated data.","PeriodicalId":370971,"journal":{"name":"Asia-Pacific Remote Sensing","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131295562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Seagrass beds provide habitat for invertebrate and fish species, many of which are economically important. In addition, they perform important physical functions such as trapping sediment particulates associated with dissipating wave energy, thus are helpful to maintain clear waters. We, here, generated the map of seagrass distribution using remotely sensed images to which atmospheric corrections and water column corrections had been applied. Then, the seagrass habitat distribution changes were calculated by seagrass habitat map. For this study, we selected Deukryang Bay located on the southern coast of the Korean peninsula. It is surrounded by small villages like Jinmok-ri and Ongam-ri. Zostera marina dominated at the bay, small amounts of Z. caulescens and Halophila nipponica are also distributed in this area. The results showed that image classifications to which the water column correction had been applied produced improved accuracies in all the classification algorithms we had employed. The object-based classification algorithm showed the highest accuracy, but it is effective method for the high spatial resolution remotely sensed images, consequently not suitable for monitoring changes of the long-term base. Thus, we applied the Mahalanobis distance method which had been known to suitable for medium spatial resolution images like Landsat. This study revealed that seagrass beds in the study area showed similar pattern of distribution during recent 20 years.
{"title":"Satellite-based seagrass mapping in Korean coastal waters","authors":"Jong-Kuk Choi, Keunyong Kim, Genki Terauchi","doi":"10.1117/12.2326787","DOIUrl":"https://doi.org/10.1117/12.2326787","url":null,"abstract":"Seagrass beds provide habitat for invertebrate and fish species, many of which are economically important. In addition, they perform important physical functions such as trapping sediment particulates associated with dissipating wave energy, thus are helpful to maintain clear waters. We, here, generated the map of seagrass distribution using remotely sensed images to which atmospheric corrections and water column corrections had been applied. Then, the seagrass habitat distribution changes were calculated by seagrass habitat map. For this study, we selected Deukryang Bay located on the southern coast of the Korean peninsula. It is surrounded by small villages like Jinmok-ri and Ongam-ri. Zostera marina dominated at the bay, small amounts of Z. caulescens and Halophila nipponica are also distributed in this area. The results showed that image classifications to which the water column correction had been applied produced improved accuracies in all the classification algorithms we had employed. The object-based classification algorithm showed the highest accuracy, but it is effective method for the high spatial resolution remotely sensed images, consequently not suitable for monitoring changes of the long-term base. Thus, we applied the Mahalanobis distance method which had been known to suitable for medium spatial resolution images like Landsat. This study revealed that seagrass beds in the study area showed similar pattern of distribution during recent 20 years.","PeriodicalId":370971,"journal":{"name":"Asia-Pacific Remote Sensing","volume":"02 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129835865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Kuroshio is the western boundary current of the North Pacific Ocean. Kuroshio intrudes into the north of South China Sea (SCS) through the Luzon Strait. The intruded Kuroshio often forms an anticyclonic current loop north of the Luzon Strait. Previous studies confirmed co-variability of sea surface temperature (SST) influence on the local atmosphere. In this study, by using satellite data and numerical modeling, the impact of Kuroshio intrusion (KI) entering north Luzon Strait on the local atmosphere is investigated. First, satellite data analysis shows that KI causing increased local sea surface temperature and enhanced intensity of wind speed and rainfall off southwestern Taiwan during easterly winds (from November to April). Moreover, based on vertical velocity (w) of atmosphere derived from reanalysis data, the result shows that higher local SST (associated with KI) triggering enhanced wind speed and rainfall by enhancing lower atmosphere vertical motion and thus decreasing vertical wind shear, which leads to intensify of vertical mixing.
{"title":"Impacts of the Kuroshio intrusion entering the Luzon Strait on the local atmosphere by satellite observations","authors":"Zhewen Zheng, Mingrui Weng, W. Fang","doi":"10.1117/12.2324044","DOIUrl":"https://doi.org/10.1117/12.2324044","url":null,"abstract":"The Kuroshio is the western boundary current of the North Pacific Ocean. Kuroshio intrudes into the north of South China Sea (SCS) through the Luzon Strait. The intruded Kuroshio often forms an anticyclonic current loop north of the Luzon Strait. Previous studies confirmed co-variability of sea surface temperature (SST) influence on the local atmosphere. In this study, by using satellite data and numerical modeling, the impact of Kuroshio intrusion (KI) entering north Luzon Strait on the local atmosphere is investigated. First, satellite data analysis shows that KI causing increased local sea surface temperature and enhanced intensity of wind speed and rainfall off southwestern Taiwan during easterly winds (from November to April). Moreover, based on vertical velocity (w) of atmosphere derived from reanalysis data, the result shows that higher local SST (associated with KI) triggering enhanced wind speed and rainfall by enhancing lower atmosphere vertical motion and thus decreasing vertical wind shear, which leads to intensify of vertical mixing.","PeriodicalId":370971,"journal":{"name":"Asia-Pacific Remote Sensing","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125838819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An essential feature of remote sensing and digital photogrammetric processes is image compression and communication over digital links. This paper investigates the probability of using a convolution-deconvolution method as a pre-post-processing step in standard digital image compression and restoration. As such, the paper relates to image coding and compression systems whereby an original image can be transmitted or stored in a convolved (i.e. blurred) representation which renders it more compressible. The image is then thoroughly restored to its original state by reversing the convolution process. The compressibility of an image increases with blurring, whereby the relation between the compression ratio (CR) and the blurring scale is almost linear. Hence, by convolving by way of a localised response function (i.e. a linear kernel) and thereby blurring an image before compression, the CR will increase accordingly. In this novel process the response function is applied to a fractal one-dimensional representation of a given image. A blurred image is thus created, which can be shown to contain the details of the original image and thereby restored by reversing the blurring process. The implications of increased CR are examined in terms of the quality of the reconstructed images.
{"title":"A convolution-deconvolution method for improved storage and communication of remotely-sensed image data","authors":"G. Scarmana, K. Mcdougall","doi":"10.1117/12.2324451","DOIUrl":"https://doi.org/10.1117/12.2324451","url":null,"abstract":"An essential feature of remote sensing and digital photogrammetric processes is image compression and communication over digital links. This paper investigates the probability of using a convolution-deconvolution method as a pre-post-processing step in standard digital image compression and restoration. As such, the paper relates to image coding and compression systems whereby an original image can be transmitted or stored in a convolved (i.e. blurred) representation which renders it more compressible. The image is then thoroughly restored to its original state by reversing the convolution process. The compressibility of an image increases with blurring, whereby the relation between the compression ratio (CR) and the blurring scale is almost linear. Hence, by convolving by way of a localised response function (i.e. a linear kernel) and thereby blurring an image before compression, the CR will increase accordingly. In this novel process the response function is applied to a fractal one-dimensional representation of a given image. A blurred image is thus created, which can be shown to contain the details of the original image and thereby restored by reversing the blurring process. The implications of increased CR are examined in terms of the quality of the reconstructed images.","PeriodicalId":370971,"journal":{"name":"Asia-Pacific Remote Sensing","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124737794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The newly launched (November 18, 2017) polar-orbiting satellite of the Joint Polar Satellite System (JPSS-1), now transitioned to NOAA-20, is the follow-on mission to the SNPP (Suomi National Polar-orbiting Partnership) satellite, launched six years ago. NOAA-20 leads SNPP by a half orbit or about 50 minutes. The Visible Infrared Imaging Radiometer Suite (VIIRS) is a key sensor onboard both NOAA-20 and SNPP spacecraft with nearly identical band spectral responses. Similar to the heritage sensor MODIS, VIIRS has on-board calibration components including a solar diffuser (SD) and a solar diffuser stability monitor (SDSM) for the reflective solar bands (RSB), a V-groove blackbody for the thermal emissive bands (TEB), and a space view (SV) as background reference for calibration. This study provides an initial assessment of calibration of the NOAA-20 VIIRS reflective solar bands (RSB) by intercomparison with measurements from SNPP VIIRS using various vicarious approaches. The first approach is based on a double difference method using observations from simultaneous nadir overpasses (SNO) with Aqua MODIS. The second is from the collected reflectances over the widely used Liby-4 desert site from 16-day repeatable orbits so each data point has the same viewing geometry relative to the site. The third approach is to use the frequent overpasses over the Dome C snow site. Results of this study provide useful information on NOAA-20 VIIRS post-launch calibration assessment and preliminary analysis of its calibration stability and consistency for the first 6 months.
{"title":"Initial assessment of radiometric performance of N20 VIIRS reflective solar bands using vicarious approaches","authors":"A. Wu, T. Chang, X. Xiong, C. Cao","doi":"10.1117/12.2324756","DOIUrl":"https://doi.org/10.1117/12.2324756","url":null,"abstract":"The newly launched (November 18, 2017) polar-orbiting satellite of the Joint Polar Satellite System (JPSS-1), now transitioned to NOAA-20, is the follow-on mission to the SNPP (Suomi National Polar-orbiting Partnership) satellite, launched six years ago. NOAA-20 leads SNPP by a half orbit or about 50 minutes. The Visible Infrared Imaging Radiometer Suite (VIIRS) is a key sensor onboard both NOAA-20 and SNPP spacecraft with nearly identical band spectral responses. Similar to the heritage sensor MODIS, VIIRS has on-board calibration components including a solar diffuser (SD) and a solar diffuser stability monitor (SDSM) for the reflective solar bands (RSB), a V-groove blackbody for the thermal emissive bands (TEB), and a space view (SV) as background reference for calibration. This study provides an initial assessment of calibration of the NOAA-20 VIIRS reflective solar bands (RSB) by intercomparison with measurements from SNPP VIIRS using various vicarious approaches. The first approach is based on a double difference method using observations from simultaneous nadir overpasses (SNO) with Aqua MODIS. The second is from the collected reflectances over the widely used Liby-4 desert site from 16-day repeatable orbits so each data point has the same viewing geometry relative to the site. The third approach is to use the frequent overpasses over the Dome C snow site. Results of this study provide useful information on NOAA-20 VIIRS post-launch calibration assessment and preliminary analysis of its calibration stability and consistency for the first 6 months.","PeriodicalId":370971,"journal":{"name":"Asia-Pacific Remote Sensing","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114277095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Land surface albedo is considered to be a fundamental quantity for accurate assessment of the surface energy budget. Whereas satellite products usually provide total surface albedo, land surface models and numerous applications often require a separation of snow-free surface albedos of the vegetation canopy and the underlying bare soil. In this paper, a simplified non-linear spectral mixture model (NSM) was firstly presented to describe the canopy radiative transfer process. The soil background’s contribution on canopy albedo was approximated using an illumination factor, which is the canopy transmittance. Then, a framework was designed to retrieve the soil VIS albedo using the NSM model, MODIS BRDF (MCD43A3), leaf area index (LAI) (MCD15A2H) and clumping index (CI) products. Finally, the global broadband visible albedo of soil background was indirectly validated using the ECOCLIMAP data, with an R2 value of 0.889. Therefore, the simplified NSM model is accurate to simulate canopy visible albedo and also to retrieve soil background’s albedo, and the first global snow-free albedo of soil background at 500-m resolution is reliable.
{"title":"Dynamic mapping of broadband visible albedo of soil background at global 500-m scale from MODIS satellite products","authors":"Liangyun Liu, Xiao Zhang","doi":"10.1117/12.2324659","DOIUrl":"https://doi.org/10.1117/12.2324659","url":null,"abstract":"Land surface albedo is considered to be a fundamental quantity for accurate assessment of the surface energy budget. Whereas satellite products usually provide total surface albedo, land surface models and numerous applications often require a separation of snow-free surface albedos of the vegetation canopy and the underlying bare soil. In this paper, a simplified non-linear spectral mixture model (NSM) was firstly presented to describe the canopy radiative transfer process. The soil background’s contribution on canopy albedo was approximated using an illumination factor, which is the canopy transmittance. Then, a framework was designed to retrieve the soil VIS albedo using the NSM model, MODIS BRDF (MCD43A3), leaf area index (LAI) (MCD15A2H) and clumping index (CI) products. Finally, the global broadband visible albedo of soil background was indirectly validated using the ECOCLIMAP data, with an R2 value of 0.889. Therefore, the simplified NSM model is accurate to simulate canopy visible albedo and also to retrieve soil background’s albedo, and the first global snow-free albedo of soil background at 500-m resolution is reliable.","PeriodicalId":370971,"journal":{"name":"Asia-Pacific Remote Sensing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128530336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Indonesian Throughflow (ITF) is one of the key ocean current in considering global climate change because ITF transports substantial heat content from the Western Pacific tropical zone to the Eastern Indian Ocean tropical zone. However, the field observation system at the site is very few, and the detail of ITF is not clear. Meanwhile, Japanese geostationary weather satellite, "Himawari-8", which has been in full operation since July 2015, has the ability to observe the hemisphere including the ITF area from visible to infrared radiation at 10 minute intervals. In this research, techniques to estimate the flow distribution of ITF were discussed taking advantage of the features of such high frequency observation. Specifically, surface velocity was estimated by MCC (Maximum Cross Correlation) method using multiple SST (2 km spatial resolution, 1 hour average interval, Level 3) data of P-tree System provided by JAXA. As a result, the flow velocity of 0.5 m/s or more was estimated using the data of August 2017 in the Major Straits of the ITF such as the Lombok Strait. The estimate is consistent with the field observation value measured by ADCP. By using such high-frequency latest satellite data, the possibility of quantifying short-term coastal environment change was shown.
{"title":"The estimation of surface flow velocity for Indonesian flow (ITF) using Himawari-8 SST data","authors":"Y. Sakuno, N. Kurokawa, N. Taniguchi","doi":"10.1117/12.2324389","DOIUrl":"https://doi.org/10.1117/12.2324389","url":null,"abstract":"The Indonesian Throughflow (ITF) is one of the key ocean current in considering global climate change because ITF transports substantial heat content from the Western Pacific tropical zone to the Eastern Indian Ocean tropical zone. However, the field observation system at the site is very few, and the detail of ITF is not clear. Meanwhile, Japanese geostationary weather satellite, \"Himawari-8\", which has been in full operation since July 2015, has the ability to observe the hemisphere including the ITF area from visible to infrared radiation at 10 minute intervals. In this research, techniques to estimate the flow distribution of ITF were discussed taking advantage of the features of such high frequency observation. Specifically, surface velocity was estimated by MCC (Maximum Cross Correlation) method using multiple SST (2 km spatial resolution, 1 hour average interval, Level 3) data of P-tree System provided by JAXA. As a result, the flow velocity of 0.5 m/s or more was estimated using the data of August 2017 in the Major Straits of the ITF such as the Lombok Strait. The estimate is consistent with the field observation value measured by ADCP. By using such high-frequency latest satellite data, the possibility of quantifying short-term coastal environment change was shown.","PeriodicalId":370971,"journal":{"name":"Asia-Pacific Remote Sensing","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115032827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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