“…A NIR water vapour absorption band located at 900 nm with a nearby window band located at 885 nm is employed to retrieve PWV estimates over ocean, land and clouds in the daytime (Xu and Liu, 2021a). The retrieval approach relies significantly on the differential absorption method, relating the atmospheric water vapour retrievals to the measured radiance ratio between the absorption channel centred at 900 nm against the near-by window band centred at 885 nm (Xu and Liu, 2021a). A neutral network as well as a matrix operator model (MOMO) are utilized (Xu and Liu, 2021a).…”
Section: Olci Instrumentmentioning
confidence: 99%
“…The retrieval approach relies significantly on the differential absorption method, relating the atmospheric water vapour retrievals to the measured radiance ratio between the absorption channel centred at 900 nm against the near-by window band centred at 885 nm (Xu and Liu, 2021a). A neutral network as well as a matrix operator model (MOMO) are utilized (Xu and Liu, 2021a). The retrieval band information of the OLCI instrument is shown in detail in Table 2.…”
Australia is a region of high sensitivity to the El Niño–Southern Oscillation events in association with atmospheric water vapour, which is an essential atmospheric parameter in hydrological cycle, energy transport and climate monitoring. In this article, we thoroughly validated the quality of multisource precipitable water vapour (PWV) products sourced from ERA5 reanalysis, advanced Medium Resolution Spectral Imager (MERSI‐II) sensor on the FY‐3D satellite, Ocean and Land Colour Instrument (OLCI) sensor on the Sentinel‐3A and Sentinel‐3B satellites, and Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the Aqua and Terra satellites. The PWV estimates measured by 453 in situ Global Positioning System (GPS) sites from 1 June 2019 to 31 May 2020 over Australia were employed as the common reference. The validation results show that all the reanalysis‐based and satellite‐based PWV products had a good agreement with reference GPS‐based PWV data. ERA5 reanalysis had the highest PWV accuracy with a root‐mean‐square error (RMSE) of 2.016 mm and a relative RMSE (RRMSE) of 12.038%, while the MODIS/Terra instrument had the lowest PWV accuracy (RMSE = 4.903 mm and RRMSE = 34.187%). In contrast to the MERSI‐II/FY‐3D instrument that underestimated PWV, the PWV data from ERA5, OLCI/Sentinel‐3A, OLCI/Sentinel‐3B, MODIS/Aqua and MODIS/Terra overestimated the water vapour values. The quality of reanalysis‐based and satellite‐based PWV measurements showed significant dependencies on several variables – location, PWV, solar zenith angle, season, elevation, land surface cover and latitude. It is expected that this research can help enhance the understanding of the quality of reanalysis and satellite water vapour products in Australia, that is, ERA5, OLCI/Sentinel‐3A, OLCI/Sentinel‐3B, MODIS/Aqua and MODIS/Terra. This work could provide an insight into improving the accuracy of reanalysis‐based and satellite‐based PWV data products after exploring the dependency factors that affect PWV performance as presented in our work.
“…A NIR water vapour absorption band located at 900 nm with a nearby window band located at 885 nm is employed to retrieve PWV estimates over ocean, land and clouds in the daytime (Xu and Liu, 2021a). The retrieval approach relies significantly on the differential absorption method, relating the atmospheric water vapour retrievals to the measured radiance ratio between the absorption channel centred at 900 nm against the near-by window band centred at 885 nm (Xu and Liu, 2021a). A neutral network as well as a matrix operator model (MOMO) are utilized (Xu and Liu, 2021a).…”
Section: Olci Instrumentmentioning
confidence: 99%
“…The retrieval approach relies significantly on the differential absorption method, relating the atmospheric water vapour retrievals to the measured radiance ratio between the absorption channel centred at 900 nm against the near-by window band centred at 885 nm (Xu and Liu, 2021a). A neutral network as well as a matrix operator model (MOMO) are utilized (Xu and Liu, 2021a). The retrieval band information of the OLCI instrument is shown in detail in Table 2.…”
Australia is a region of high sensitivity to the El Niño–Southern Oscillation events in association with atmospheric water vapour, which is an essential atmospheric parameter in hydrological cycle, energy transport and climate monitoring. In this article, we thoroughly validated the quality of multisource precipitable water vapour (PWV) products sourced from ERA5 reanalysis, advanced Medium Resolution Spectral Imager (MERSI‐II) sensor on the FY‐3D satellite, Ocean and Land Colour Instrument (OLCI) sensor on the Sentinel‐3A and Sentinel‐3B satellites, and Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the Aqua and Terra satellites. The PWV estimates measured by 453 in situ Global Positioning System (GPS) sites from 1 June 2019 to 31 May 2020 over Australia were employed as the common reference. The validation results show that all the reanalysis‐based and satellite‐based PWV products had a good agreement with reference GPS‐based PWV data. ERA5 reanalysis had the highest PWV accuracy with a root‐mean‐square error (RMSE) of 2.016 mm and a relative RMSE (RRMSE) of 12.038%, while the MODIS/Terra instrument had the lowest PWV accuracy (RMSE = 4.903 mm and RRMSE = 34.187%). In contrast to the MERSI‐II/FY‐3D instrument that underestimated PWV, the PWV data from ERA5, OLCI/Sentinel‐3A, OLCI/Sentinel‐3B, MODIS/Aqua and MODIS/Terra overestimated the water vapour values. The quality of reanalysis‐based and satellite‐based PWV measurements showed significant dependencies on several variables – location, PWV, solar zenith angle, season, elevation, land surface cover and latitude. It is expected that this research can help enhance the understanding of the quality of reanalysis and satellite water vapour products in Australia, that is, ERA5, OLCI/Sentinel‐3A, OLCI/Sentinel‐3B, MODIS/Aqua and MODIS/Terra. This work could provide an insight into improving the accuracy of reanalysis‐based and satellite‐based PWV data products after exploring the dependency factors that affect PWV performance as presented in our work.
“…Training activities in making digital maps based on spatial data are carried out in the form of workshops using Sofware Autodesk MAP is one of the Geographic Information System (GIS) based software open source licensed under the GNU General Public License that can be run on a variety of operating systems (Sha et al, 2021;Shen et al, 2022;Sutton et al, 2021). Autodesk MAP Easy to operate by providing common functions and features (Wu & Hifi, 2021;Xu & Liu, 2021;Zeeshan et al, 2021).…”
This program designed to enhance the skills and knowledge of students and teaching staff at Pesantren Rahmatullah (Islamic boarding school) regarding the use of Geographic Information System (GIS) technology in boundary mapping. The initiative was launched recognizing the importance of understanding territorial boundaries in the context of natural resource management, development planning, and disaster mitigation in the surrounding areas. The training includes a series of theoretical sessions and field practices. The first stage involves learning how to gather coordinate points in the field using the Garmin 60 CSX GPS device. The second stage focuses on field data processing and coordinate data correction, covering basic GIS knowledge, introduction to GIS software, and usage of Autodesk Map software and Arc GIS 10.8. The third stage is the scaled printing of processed field data. The duration of the training is one semester, following the curriculum set by the Rahmatullah Islamic boarding school. This training aims to equip participants with the necessary skills to produce accurate and informative boundary maps, which will later serve as tools for decision-making and strategic planning at Rahmatullah Lempake Islamic boarding school in Samarinda. Additionally, this activity also aims to enhance the boarding school’s awareness and capacity in utilizing geographic information technology for educational and environmental management purposes.
“…In 2019, Sridevi studied the relationship between average GNSS-PWV and GNSS station height in India and found that the two were inversely proportional [8] . In 2021, XU studied the Marine and land colorimeter mounted on the Sentinel-3 series satellites and compared it with GNSS PWV and found that the root-mean-square error of PWV obtained by them was 3.154mm [9] . In 2022, Ghaffari used two different machine learning methods to model the GNSS PWV and found that the support vector machine training method was the most accurate in predicting PWV [10] .…”
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