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21 pages, 636 KiB

Open AccessArticle

Data Collection for Target Localization in Ocean Monitoring Radar-Communication Networks

by Yuan Liu, Shengjie Zhao, Fengxia Han, Mengqiu Chai, Hao Jiang and Hongming Zhang

Remote Sens. 2023, 15(21), 5126; https://doi.org/10.3390/rs15215126 - 26 Oct 2023

Cited by 4 | Viewed by 1367

Abstract

With the ongoing changes in global climate, ocean data play a crucial role in understanding the complex variations in the earth system. These variations pose significant challenges to human efforts in addressing the changes. As a data hub for the satellite geodetic technique, [...] Read more.

With the ongoing changes in global climate, ocean data play a crucial role in understanding the complex variations in the earth system. These variations pose significant challenges to human efforts in addressing the changes. As a data hub for the satellite geodetic technique, unmanned aerial vehicles (UAVs) instill new vitality into ocean data collection due to their flexibility and mobility. At the same time, the dual-functional radar-communication (DFRC) system is considered a promising technology to empower ubiquitous communication and high-accuracy localization. In this paper, we explore a new fusion of UAV and DFRC to assist data acquisition in the ocean surveillance scenario. The floating buoys transmit uplink data transmission to the UAV with non-orthogonal multiple access (NOMA) and attempt to localize the target cooperatively. With the mobility of the UAV and power control at the buoys, the system throughput and the target localization performance can be improved simultaneously. To balance the communication and sensing performance, a two-objective optimization problem is formulated by jointly optimizing the UAV’s location and buoy’s transmit power to maximize the system throughput and minimize the attainable localization mean-square error. We propose a joint communication and radar-sensing many-objective optimization (CRMOP) algorithm to meliorate the communication and radar-sensing performance simultaneously. Simulation results demonstrate that compared with the baseline, the proposed algorithm achieves superior performance in balancing the system throughput and target localization. Full article

(This article belongs to the Special Issue Applications of Satellite Geodesy in Hydrology, Glaciology, and Oceanography)

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18 pages, 8645 KiB

Open AccessArticle

Assessment of Variability and Attribution of Drought Based on GRACE in China from Three Perspectives: Water Storage Component, Climate Change, Water Balance

by Rong Wu, Chengyuan Zhang, Yuli Li, Chenrui Zhu, Liang Lu, Chenfeng Cui, Zhitao Zhang, Shuo Wang, Jiangdong Chu and Yongxiang Li

Remote Sens. 2023, 15(18), 4426; https://doi.org/10.3390/rs15184426 - 8 Sep 2023

Cited by 1 | Viewed by 1714

Abstract

Understanding how drought is impacted by both natural and human influences is crucial to the sustainable utilization and protection of water resources. We established a drought severity index (DSI) based on the terrestrial water storage anomaly (TWSA) derived from the GRACE satellite to [...] Read more.

Understanding how drought is impacted by both natural and human influences is crucial to the sustainable utilization and protection of water resources. We established a drought severity index (DSI) based on the terrestrial water storage anomaly (TWSA) derived from the GRACE satellite to detect drought characteristics and trends over ten major river basins in China from 2002 to 2017. The influence of natural factors (terrestrial water storage components, precipitation, evapotranspiration, runoff, NDVI, and teleconnection factors (ENSO, PDO, NAO, and AO)) and a human factor (LULC) on drought were investigated and quantified from the perspective of water storage components based on the Theil–Sen trend and Mann–Kendall test method, the perspective of climate change based on cross wavelet transforms, and the perspective of water balance based on Random Forest. The results indicated that (1) almost all humid and arid basins experienced major drought periods during 2002–2006 and 2014–2017, respectively. The southern IRB and central YZRB regions exhibited notable declines in DSI trends, while the majority of the HLRB, IRB, LRB, YRB, HRB, and SWRB experienced significant increases in DSI trends; (2) abnormal groundwater decreases were the main cause of drought triggered by insufficient terrestrial water storage in most basins; (3) ENSO was the strongest teleconnection factor in most humid basins, and NAO, PDO, and AO were the strongest teleconnection factors in the arid basins and PRB. Most significant resonance cycles lasted 12–64 months in 2005–2014; and (4) the influence of an anthropogenic driver (LULC) has become as important as, or more important than, natural factors (runoff and teleconnection factors) on hydrological drought. Full article

(This article belongs to the Special Issue Applications of Satellite Geodesy in Hydrology, Glaciology, and Oceanography)

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19 pages, 12951 KiB

Open AccessArticle

Spatiotemporal Evaluation of the Flood Potential Index and Its Driving Factors across the Volga River Basin Based on Combined Satellite Gravity Observations

by Zhengbo Zou, Yu Li, Lilu Cui, Chaolong Yao, Chuang Xu, Maoqiao Yin and Chengkang Zhu

Remote Sens. 2023, 15(17), 4144; https://doi.org/10.3390/rs15174144 - 24 Aug 2023

Cited by 3 | Viewed by 1615

Abstract

Floods have always threatened the survival and development of human beings. To reduce the adverse effects of floods, it is very important to understand the influencing factors of floods and their formation mechanisms. In our study, we integrated the Gravity Recovery and Climate [...] Read more.

Floods have always threatened the survival and development of human beings. To reduce the adverse effects of floods, it is very important to understand the influencing factors of floods and their formation mechanisms. In our study, we integrated the Gravity Recovery and Climate Experiment and its Follow-On and Swarm solutions to estimate an uninterrupted 19-year flood potential index (FPI) time series, discussed the spatiotemporal distribution characteristics of the FPI and monitored major floods in the Volga River basin (VRB) from 2003 to 2021. Finally, we analyzed the relationship between the FPI and hydrometeorological factors to comprehend the flood formation mechanism. The results show that data fusion has reduced the uncertainty of terrestrial water storage change (TWSC), and the TWSC from the combined satellite gravity observations has a good consistency with that from the Global Land Data Assimilation System model (correlation coefficient = 0.92). During the study period, two major floods (June 2005 and May 2018) occurred in the VRB. The FPI has a significant seasonal change characteristic, and shows a high flood risk in spring and a low one in autumn. With regards to spatial distribution, the flood risk is increasing in the north (increasing rate = 0.1) and decreasing in the south (decreasing rate = 0.39). Snow water equivalent (SWE, correlation coefficient = 0.75) has a stronger correlation with the FPI than precipitation (PPT, correlation coefficient = 0.46), which is attributed to the recharge of SWE on water resources greater than that of PPT. The rising surface temperature (ST) speeds up snow melt, resulting in excessive groundwater and soil moisture, and the flood risk greatly increases at this time. The process lasts about three months. Therefore, except for PPT, ST is also a climatic factor leading to the floods in the VRB. Our study provides a reference for flood research in high-latitude regions. Full article

(This article belongs to the Special Issue Applications of Satellite Geodesy in Hydrology, Glaciology, and Oceanography)

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21 pages, 9401 KiB

Open AccessArticle

Evaluation of Terrestrial Water Storage and Flux in North China by Using GRACE Combined Gravity Field Solutions and Hydrometeorological Models

by Tengfei Feng, Yunzhong Shen, Qiujie Chen, Fengwei Wang and Kunpu Ji

Remote Sens. 2023, 15(10), 2536; https://doi.org/10.3390/rs15102536 - 12 May 2023

Cited by 1 | Viewed by 1742

Abstract

To enrich the understanding of the dynamic evolution of the water resources in North China, terrestrial water storage anomalies (TWSA) from January 2003 to June 2017 are derived using the new GRACE time-variable gravity field model Tongji-GraceCom. Additionally, the spatiotemporal characteristics of terrestrial [...] Read more.

To enrich the understanding of the dynamic evolution of the water resources in North China, terrestrial water storage anomalies (TWSA) from January 2003 to June 2017 are derived using the new GRACE time-variable gravity field model Tongji-GraceCom. Additionally, the spatiotemporal characteristics of terrestrial water fluxes (TWF) at multiple time scales are analyzed based on the water budget theory in conjunction with hydrometeorological and statistical data. The results show that the quality of the Tongji-GraceCom model is superior to the state-of-art spherical harmonic models (CSR RL06 and JPL RL06), with the signal-to-noise ratio improving by 10–16%. After correcting the leakage errors with a reliable correction method, the inferred TWSA in North China presents a significant downward trend, amounting to −1.61 ± 0.05 cm/yr, with the most serious TWSA depletion mainly clustering in the south-central area. The TWFs derived from GRACE and from hydrometeorological elements are in good agreement and both exhibit significant seasonal fluctuations induced by tracking the periodic movements of meteorological factors. However, unlike precipitation which manifests in an increasing trend, both TWFs reflect the obvious decreasing trends, indicating that North China is suffering from severe water deficits, which are mainly attributed to the enhanced evaporation and extensive groundwater pumping for agricultural irrigation. Full article

(This article belongs to the Special Issue Applications of Satellite Geodesy in Hydrology, Glaciology, and Oceanography)

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17 pages, 4239 KiB

Open AccessArticle

Estimation of Root-Zone Soil Moisture in Semi-Arid Areas Based on Remotely Sensed Data

by Xiaomeng Guo, Xiuqin Fang, Qiuan Zhu, Shanhu Jiang, Jia Tian, Qingjiu Tian and Jiaxin Jin

Remote Sens. 2023, 15(8), 2003; https://doi.org/10.3390/rs15082003 - 10 Apr 2023

Cited by 6 | Viewed by 2402

Abstract

Soil moisture (SM) is a bridge between the atmosphere, vegetation and soil, and its dynamics reflect the energy exchange and transformation between the three. Among SM at different soil profiles, root zone soil moisture (RZSM) plays a significant role in vegetation growth. Therefore, [...] Read more.

Soil moisture (SM) is a bridge between the atmosphere, vegetation and soil, and its dynamics reflect the energy exchange and transformation between the three. Among SM at different soil profiles, root zone soil moisture (RZSM) plays a significant role in vegetation growth. Therefore, reliable estimation of RZSM at the regional scale is of great importance for drought warning, agricultural yield estimation, forest fire monitoring, etc. Many satellite products provide surface soil moisture (SSM) at the thin top layer of the soil, approximately 2 cm from the surface. However, the acquisition of RZSM at the regional scale is still a tough issue to solve, especially in the semi-arid areas with a lack of in situ observations. Linking the dynamics of SSM and RZSM is promising to solve this issue. The soil moisture analytical relationship (SMAR) model can relate RZSM to SSM based on a simplified soil water balance equation, which is suitable for the simulation of soil moisture mechanisms in semi-arid areas. In this study, the Xiliaohe River Basin is the study area. The SMAR model at the pixels where in situ sites were located is established, and parameters (a, b, s_w2, s_c1) at these pixels are calibrated by a genetic algorithm (GA). Then the spatial parameters are estimated by the random forest (RF) regression method with the soil, meteorological and vegetation characteristics of the study area as explanatory variables. In addition, the importance of soil, climatic and vegetation characteristics for predicting SMAR parameters is analyzed. Finally, the spatial RZSM in the Xiliaohe River Basin is estimated by the SMAR model at the regional scale with the predicted spatial parameters, and the variation of the regional SMAR model performance is discussed. A comparison of estimated RZSM and in-situ RZSM showed that the SMAR model at the point and regional scales can both meet the RMSE benchmark from NASA of 0.06 cm³·cm⁻³, indicating that the method this study proposed could effectively estimate RZSM in semi-arid areas based on remotely sensed SSM data. Full article

(This article belongs to the Special Issue Applications of Satellite Geodesy in Hydrology, Glaciology, and Oceanography)

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21 pages, 5598 KiB

Open AccessArticle

Improved the Characterization of Flood Monitoring Based on Reconstructed Daily GRACE Solutions over the Haihe River Basin

by Shengkun Nie, Wei Zheng, Wenjie Yin, Yulong Zhong, Yifan Shen and Kezhao Li

Remote Sens. 2023, 15(6), 1564; https://doi.org/10.3390/rs15061564 - 13 Mar 2023

Cited by 4 | Viewed by 2493

Abstract

Flood events have caused huge disasters with regard to human life and economic development, especially short-term flood events that have occurred in recent years. Gravity Recovery and Climate Experiment (GRACE) satellites can directly detect the spatiotemporal characteristics of terrestrial water storage anomalies (TWSA), [...] Read more.

Flood events have caused huge disasters with regard to human life and economic development, especially short-term flood events that have occurred in recent years. Gravity Recovery and Climate Experiment (GRACE) satellites can directly detect the spatiotemporal characteristics of terrestrial water storage anomalies (TWSA), which play an important role in capturing flood signals. However, the monthly resolution of GRACE-derived TWSA limits its application in monitoring sub-monthly flood events. Therefore, this paper first reconstructs the daily TWSA based on a statistical model with near real-time precipitation and temperature as input variables, and then three daily flood monitoring indexes are developed based on the reconstructed TWSA. Furthermore, these indexes are employed to evaluate the temporal and spatial characteristics of the 2016 short-term flood event in the Haihe River basin (HRB), including the flood potential index (FPI), water storage deficit index (WSDI), and combined climate deviation index (CCDI). In contrast to previous studies, the temporal resolution of TWSA-based indexes is improved from the monthly scale to the daily scale, which largely improves the temporal characterization of flood monitoring. Results demonstrate that (1) among ten kinds of “Temperature-Precipitation” combinations, the reconstructed TWSA based on CN05.1-CN05.1 match well with the GRACE TWSA, as well as publicly available daily TWSA datasets with a Nash-Sutcliffe efficiency coefficient (NSE) of 0.96 and 0.52 ~ 0.81 respectively. (2) The short-term flood characteristics can be better characterized by the reconstructed daily TWSA based on CN05.1-CN05.1, reaching the peak of 216.19 mm on July 20 in the flood center. Additionally, the spatial characteristics of the equivalent water height (EWH) are detected to evolve from southwest to northeast during the short-term flood. (3) FPI, WSDI, and CCDI are proven to be effective in monitoring flood events in the HRB, which validates the reliability of the reconstructed daily TWSA. Moreover, compared to the 56% and 66% coverage of damage quantified by FPI and CCDI, the 45% damage coverage of the flood mapped by WSDI is more consistent with the governmental reports within the HRB. This paper is expected to provide a valuable reference for the assessment of short-term events caused by extreme climate change. Full article

(This article belongs to the Special Issue Applications of Satellite Geodesy in Hydrology, Glaciology, and Oceanography)

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18 pages, 29438 KiB

Open AccessArticle

Remote Sensing Monitoring and Analysis of Jinwuco Lateral Moraine Landslide-Glacial Lake Outburst in Southeast Tibet

by Yaping Gao, Wenguang Yang, Rui Guo and Liming Jiang

Remote Sens. 2023, 15(6), 1475; https://doi.org/10.3390/rs15061475 - 7 Mar 2023

Cited by 4 | Viewed by 2694

Abstract

On 25 June 2020, a glacial lake outburst flood (GLOF) occurred in Jinwuco, Nidou Zangbo, and southeast Tibet, causing catastrophic damage to multiple infrastructures such as roads, bridges, and farmlands in the surrounding and downstream areas. Due to the lack of long-term monitoring [...] Read more.

On 25 June 2020, a glacial lake outburst flood (GLOF) occurred in Jinwuco, Nidou Zangbo, and southeast Tibet, causing catastrophic damage to multiple infrastructures such as roads, bridges, and farmlands in the surrounding and downstream areas. Due to the lack of long-term monitoring of glacial lake and glacier changes in the region and the surrounding surface, the spatial and temporal evolutionary characteristics and triggering factors of the disaster still need to be determined. Here, we combine multi-temporal optical remote sensing image interpretation, surface deformation monitoring with synthetic aperture radar (SAR)/InSAR, meteorological observation data, and corresponding soil moisture change information to systematically analyze the spatial and temporal evolution characteristics and triggering factors of this GLOF disaster. Optical images taken between 1987 and 2020 indicate that the glacial lake’s initial area of 0.39 km² quickly grew to 0.56 km², then plummeted to 0.26 km² after the catastrophe. Meanwhile, we found obvious signs of slippage beside the lateral moraine at the junction of the glacier’s terminus and the glacial lake. The pixel offset tracking (POT) results based on SAR images acquired before and after the disaster reveal that the western lateral moraine underwent a 40 m line of sight (LOS) deformation. The small baseline subset InSAR (SBAS-InSAR) results from 2017 to 2021 show that the cumulative deformation of the slope around the lateral moraine increased in the rainy season before the disaster, with a maximum cumulative deformation of −52 mm in 120 days and gradually stabilized after the disaster. However, there are three long-term deformation areas on the slope above it, showing an increasing trend after the disaster, with cumulative deformation exceeding −30 mm during the monitoring period. The lateral moraine collapse occurred in a warm climate with continuous and intense precipitation, and the low backscatter intensity prior to the slide suggests that the soil was very moist. Intense rainfall is thought to be the catalyst for lateral moraine collapse, whereas the lateral moraine falling into the glacier lake is the direct cause of the GLOF. This study shows that the joint active–passive remote sensing technique can accurately obtain the spatial and temporal evolution characteristics and triggering factors of GLOF. It is helpful to understand the GLOF event caused by the slide of lateral moraine more comprehensively, which is essential for further work related to glacial lake hazard assessment. Full article

(This article belongs to the Special Issue Applications of Satellite Geodesy in Hydrology, Glaciology, and Oceanography)

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18 pages, 48650 KiB

Open AccessArticle

Inclinometer and Improved SBAS Methods with a Random Forest for Monitoring Landslides and Anchor Degradation in Otoyo Town, Japan

by Noha Ismail Medhat, Masa-Yuki Yamamoto and Yoshiharu Ichihashi

Remote Sens. 2023, 15(2), 441; https://doi.org/10.3390/rs15020441 - 11 Jan 2023

Cited by 7 | Viewed by 2741

Abstract

Kochi Prefecture is located in an active zone of Japan that is frequently subjected to landslides due to heavy precipitation in typhoon seasons. Slow-moving landslides have been reported by both the local prefectural authorities and the National Government of Japan. We observed landslide [...] Read more.

Kochi Prefecture is located in an active zone of Japan that is frequently subjected to landslides due to heavy precipitation in typhoon seasons. Slow-moving landslides have been reported by both the local prefectural authorities and the National Government of Japan. We observed landslide movements in Otoyo Town by using ground- and satellite-based tools. Despite the high cost of establishing a borehole inclinometer survey to obtain accurate ground-based measurements, no previous InSAR study has been conducted in Otoyo Town, and the capacity for regional discrimination between active and inactive slow-moving landslides when using these tools remains unclear. We found that the horizontal velocity component was dominant at a rate of 21.4 mm/year across the whole of Otoyo Town. Satellite-based monitoring of ground-anchor efficiency may be possible in combination with ground-based inclinometer surveys. Three types of land cover are present in the study area—urban, field, and forests—and we selected a random forest (RF) model to extract low-coherence pixels by using optical and radar satellite sensors to identify important features and precisely remove pixels causing decorrelation. Long-term monitoring results from ground-based surveys, including inclinometer (boreholes) and anchor tension distribution data, were compared with the results of synthetic radar by using coherence-based small baseline subset (CB-SBAS) measurements. Generally, landslide occurrence was investigated across the whole of Otoyo Town, and we specifically evaluated the reliability of InSAR measurements in the Kawai landslide as a study site scale. The activity of the Kawai landslide channel was evaluated with borehole inclinometer displacement measurements (15.46 mm) and an anchor pressure survey (736 kN) from 2016 to 2019, as well as the steady state of the area (1.7 mm for the borehole inclinometer and 175 kN for the anchor pressure measurements), although a high cumulative precipitation of 3520 mm was reached during 2020 due to the ground anchor efficiency, which showed a consistent tendency with respect to the InSAR displacement measurements (14 mm during 2018 and 2019 and 0.7 mm during 2020). This comparison showed a consistent time-series displacement correlation, which was strengthened after introducing the RF mask into the analysis procedure, as the RF model correction reduced the standard deviation from the line-of-sight (LoS) average velocity estimation by 1.9 mm/year. Our research will help mitigate landslide impacts in Otoyo Town and its surroundings. Full article

(This article belongs to the Special Issue Applications of Satellite Geodesy in Hydrology, Glaciology, and Oceanography)

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19 pages, 3811 KiB

Open AccessArticle

Reconstruction of Annual Glacier Mass Balance from Remote Sensing-Derived Average Glacier-Wide Albedo

by Zhimin Zhang, Liming Jiang, Yafei Sun, Pascal Sirguey, Marie Dumont, Lin Liu, Ning Gao and Songfeng Gao

Remote Sens. 2023, 15(1), 31; https://doi.org/10.3390/rs15010031 - 21 Dec 2022

Cited by 2 | Viewed by 2587

Abstract

Annual mass balance is an important reflection of glacier status that is also very sensitive to climate fluctuations. However, there is no effective and universal albedo-based method for the reconstruction of annual mass balance due to the scarcity of field observations. Here, we [...] Read more.

Annual mass balance is an important reflection of glacier status that is also very sensitive to climate fluctuations. However, there is no effective and universal albedo-based method for the reconstruction of annual mass balance due to the scarcity of field observations. Here, we present an improved albedo–mass balance (IAMB) method to estimate annual glacier surface mass balance series using remote sensing techniques. The averaged glacier-wide albedo derived with the MODImLab algorithm during the summer season provides an effective proxy of the annual mass change. Defined as the variation in the albedo as a function of elevation change, the altitude–albedo gradient (

\partial z / \partial α

) can be obtained from a glacier digital elevation model (DEM) and optical images. The Chhota Shigri glacier situated in the western Himalayas was selected to test and assess the accuracy of this method over the period from 2003 to 2014. Reconstructed annual mass budgets correlated well with those from the observed records, with an average difference and root mean square error (RMSE) of −0.75 mm w.e. a⁻¹ and 274.91 mm w.e. a⁻¹, respectively, indicating that the IAMB method holds promise for glacier mass change monitoring. This study provides a new technique for annual mass balance estimation that can be applied to glaciers with no or few mass balance observations. Full article

(This article belongs to the Special Issue Applications of Satellite Geodesy in Hydrology, Glaciology, and Oceanography)

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14 pages, 3860 KiB

Open AccessArticle

A Calibrated GPT3 (CGPT3) Model for the Site-Specific Zenith Hydrostatic Delay Estimation in the Chinese Mainland and Its Surrounding Areas

by Junyu Li, Feijuan Li, Lilong Liu, Liangke Huang, Lv Zhou and Hongchang He

Remote Sens. 2022, 14(24), 6357; https://doi.org/10.3390/rs14246357 - 15 Dec 2022

Cited by 4 | Viewed by 2103

Abstract

The prior zenith hydrostatic delay (ZHD) is an essential parameter for the Global Navigation Satellite System (GNSS) and very long baseline interferometry (VLBI) high-precision data processing. Meanwhile, the precise ZHD facilitates the separation of the high-precision zenith wet delay (ZWD) to derive precipitable [...] Read more.

The prior zenith hydrostatic delay (ZHD) is an essential parameter for the Global Navigation Satellite System (GNSS) and very long baseline interferometry (VLBI) high-precision data processing. Meanwhile, the precise ZHD facilitates the separation of the high-precision zenith wet delay (ZWD) to derive precipitable water vapor (PWV). This paper analyzes the temporal variations in the residuals between GPT3 ZHD and reference ZHD from radiosonde (RS) sites, and a calibrated GPT3 (CGPT3) model is proposed for the site-specific ZHD estimation in the Chinese mainland and its surrounding areas based on the annual, semi-annual, and diurnal variations in residuals. Based on the validation using modeling RS data, the mean absolute error (MAE) and root mean square (RMS) of the CGPT3 model are 7.3 and 9.6 mm, respectively. The validation with RS ZHD not involved in the modeling suggests that the MAE and RMS of the CGPT3 model are 7.9 and 10.2 mm, respectively. These results show improvements of 16.8%/16.8% and 14.3%/13.6%, respectively, compared with the MAE and RMS of the GPT3 model and the newly proposed model (GTrop). In addition, the CGPT3 model has excellent spatial and temporal stability in the study area. Full article

(This article belongs to the Special Issue Applications of Satellite Geodesy in Hydrology, Glaciology, and Oceanography)

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17 pages, 8408 KiB

Open AccessArticle

Impact of Errors in Environmental Correction on Gravity Field Recovery Using Interferometric Radar Altimeter Observations

by Xiaoyun Wan, Fei Wang, Hengyang Guo and Bo Liu

Remote Sens. 2022, 14(24), 6299; https://doi.org/10.3390/rs14246299 - 12 Dec 2022

Cited by 5 | Viewed by 1755

Abstract

As a new type of altimeter, interferometric radar altimeter (InRA) has significant potential in marine gravity field recovery due to its high spatial resolution. However, errors in environmental correction on gravity field recovery using InRA observations are unclear. In this study, four kinds [...] Read more.

As a new type of altimeter, interferometric radar altimeter (InRA) has significant potential in marine gravity field recovery due to its high spatial resolution. However, errors in environmental correction on gravity field recovery using InRA observations are unclear. In this study, four kinds of these errors, including wet and dry troposphere, ionosphere, and sea state bias (SSB) correction errors, are simulated. The impact of these errors on gravity field recovery are analyzed and discussed. The results show that, among the four types of errors in environmental correction, the wet troposphere and SSB have a more significant impact on the accuracy of sea surface height computing, and the wet troposphere has the most significant impact on the accuracy of gravity field recovery. The maximum error of gravity anomaly caused by the wet troposphere residual errors is nearly 2 mGal, and the relative error of the recovered gravity anomaly is around 6.42%. We can also find that SSB has a little more significant impact than dry troposphere and ionosphere, where dry troposphere and ionosphere have an almost identical impact, on DV and GA inversion accuracy. Full article

(This article belongs to the Special Issue Applications of Satellite Geodesy in Hydrology, Glaciology, and Oceanography)

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25 pages, 8551 KiB

Open AccessArticle

Water Level Change of Qinghai Lake from ICESat and ICESat-2 Laser Altimetry

by Weixiao Han, Chunlin Huang, Juan Gu, Jinliang Hou, Ying Zhang and Weizhen Wang

Remote Sens. 2022, 14(24), 6212; https://doi.org/10.3390/rs14246212 - 8 Dec 2022

Cited by 4 | Viewed by 2868

Abstract

Long-term satellite observations of the water levels of lakes are crucial to our understanding of lake hydrological basin systems. The Ice, Cloud, and Land Elevation satellite (ICESat) and ICESat-2 were employed to monitor the water level of Qinghai Lake in the hydrological basin. [...] Read more.

Long-term satellite observations of the water levels of lakes are crucial to our understanding of lake hydrological basin systems. The Ice, Cloud, and Land Elevation satellite (ICESat) and ICESat-2 were employed to monitor the water level of Qinghai Lake in the hydrological basin. The median of absolute deviation (MAD) method was exploited to remove the outliers. The results confirmed that the MAD range of ICESat was from 0.0525 to 0.2470 m, and the range of σ was from 0.0778 to 0.3662 m; the MAD range of ICESat-2 was from 0.0291 to 0.0490 m, and the range of σ was from 0.0431 to 0.0726 m; ICESat-2 was less than that of ICESat. The reference ellipsoid and geoid transfer equations were applied to convert the water level to the World Geodetic System (WGS84) and Earth Gravitational Model 2008 (EGM2008) geoid. The water level, as derived from laser altimeters, was validated by the Xiashe Hydrological Station; with ICESat, the coefficient of association (R) was 0.8419, the root mean square error (RMSE) was 0.1449 m, and the mean absolute error (MAE) was 0.1144 m; with ICESat-2, the R was 0.6917, the RMSE was 0.0531 m, and the MAE was 0.0647 m. The water levels from ICESat-2 are much more accurate than those from ICESat. The two combined laser altimeters showed that the R was 0.9931, the RMSE was 0.1309 m, and the MAE was 0.1035 m. The water level rise was 3.6584 m from 2004 to 2020. The rising rate was 0.2287 m/a. The collaborative use of the ICESat-2 and ICESat satellites made it easier to obtain the lake water levels. Full article

(This article belongs to the Special Issue Applications of Satellite Geodesy in Hydrology, Glaciology, and Oceanography)

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15 pages, 4579 KiB

Open AccessArticle

A Tropospheric Zenith Delay Forecasting Model Based on a Long Short-Term Memory Neural Network and Its Impact on Precise Point Positioning

by Huan Zhang, Yibin Yao, Mingxian Hu, Chaoqian Xu, Xiaoning Su, Defu Che and Wenjie Peng

Remote Sens. 2022, 14(23), 5921; https://doi.org/10.3390/rs14235921 - 23 Nov 2022

Cited by 8 | Viewed by 2276

Abstract

Global navigation satellite system (GNSS) signals are affected by refraction when traveling through the troposphere, which result in tropospheric delay. Generally, the tropospheric delay is estimated as an unknown parameter in GNSS data processing. With the increasing demand for GNSS real-time applications, high-precision [...] Read more.

Global navigation satellite system (GNSS) signals are affected by refraction when traveling through the troposphere, which result in tropospheric delay. Generally, the tropospheric delay is estimated as an unknown parameter in GNSS data processing. With the increasing demand for GNSS real-time applications, high-precision tropospheric delay augmentation information is vital to speed up the convergence of PPP. In this research, we estimate the zenith tropospheric delay (ZTD) from 2018 to 2019 by static precise point positioning (PPP) using the fixed position mode; GNSS observations were obtained from the National Geomatics Center of China (NGCC). Firstly, ZTD outliers were detected, and data gaps were interpolated using the K-nearest neighbor algorithm (KNN). Secondly, The ZTD differences between the KNN and periodic model were employed as input datasets to train the long short-term memory (LSTM) neural network. Finally, LSTM forecasted ZTD differences and the ZTD periodic signals were combined to recover the final forecasted ZTD results. In addition, the forecasted ZTD results were applied in static PPP as a prior constraint to reduce PPP convergence time. Numerical results show that the average root-mean-square error (RMSE) of predicting ZTD is about 1 cm. The convergence time of the PPP which was corrected by the LSTM-ZTD predictions is reduced by 13.9, 22.6, and 30.7% in the summer, autumn, and winter, respectively, over GPT2-ZTD corrected PPP and unconstrained conventional PPP for different seasons. Full article

(This article belongs to the Special Issue Applications of Satellite Geodesy in Hydrology, Glaciology, and Oceanography)

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Review

Jump to: Research, Other

30 pages, 18928 KiB

Open AccessEditor’s ChoiceReview

A Bibliometric and Visualized Analysis of Remote Sensing Methods for Glacier Mass Balance Research

by Aijie Yu, Hongling Shi, Yifan Wang, Jin Yang, Chunchun Gao and Yang Lu

Remote Sens. 2023, 15(5), 1425; https://doi.org/10.3390/rs15051425 - 3 Mar 2023

Cited by 5 | Viewed by 4404

Abstract

In recent decades, climate change has led to global warming, glacier melting, glacial lake outbursts, sea level rising, and more extreme weather, and has seriously affected human life. Remote sensing technology has advanced quickly, and it offers effective observation techniques for studying and [...] Read more.

In recent decades, climate change has led to global warming, glacier melting, glacial lake outbursts, sea level rising, and more extreme weather, and has seriously affected human life. Remote sensing technology has advanced quickly, and it offers effective observation techniques for studying and monitoring glaciers. In order to clarify the stage of research development, research hotspots, research frontiers, and limitations and challenges in glacier mass balance based on remote sensing technology, we used the tools of bibliometrics and data visualization to analyze 4817 works of literature related to glacier mass balance based on remote sensing technology from 1990 to 2021 in the Web of Science database. The results showed that (1) China and the United States are the major countries in the study of glacier mass balance based on remote sensing technology. (2) The Chinese Academy of Sciences is the most productive research institution. (3) Current research hotspots focus on “Climate change”, “Inventory”, “Dynamics”, “Model”, “Retreat”, “Glacier mass balance”, “Sea level”, “Radar”, “Volume change”, “Surface velocity”, “Glacier mapping”, “Hazard”, and other keywords. (4) The current research frontiers include water storage change, artificial intelligence, High Mountain Asia (HMA), photogrammetry, debris cover, geodetic method, area change, glacier volume, classification, satellite gravimetry, grounding line retreat, risk assessment, lake outburst flood, glacier elevation change, digital elevation model, geodetic mass balance, (DEM) generation, etc. According to the results of the visual analysis of the literature, we introduced the three commonly used methods of glacier mass balance based on remote sensing observation and summarized the research status and shortcomings of different methods in glacier mass balance. We considered that the future research trend is to improve the spatial and temporal resolution of data and combine a variety of methods and data to achieve high precision and long-term monitoring of glacier mass changes and improve the consistency of results. This research summarizes the study of glacier mass balance using remote sensing, which will provide valuable information for future research across this field. Full article

(This article belongs to the Special Issue Applications of Satellite Geodesy in Hydrology, Glaciology, and Oceanography)

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16 pages, 8310 KiB

Open AccessTechnical Note

Machine Learning-Based Calibrated Model for Forecast Vienna Mapping Function 3 Zenith Wet Delay

by Feijuan Li, Junyu Li, Lilong Liu, Liangke Huang, Lv Zhou and Hongchang He

Remote Sens. 2023, 15(19), 4824; https://doi.org/10.3390/rs15194824 - 5 Oct 2023

Cited by 2 | Viewed by 1283

Abstract

An accurate estimation of zenith wet delay (ZWD) is crucial for global navigation satellite system (GNSS) positioning and GNSS-based precipitable water vapor (PWV) inversion. The forecast Vienna Mapping Function 3 (VMF3-FC) is a forecast product provided by the Vienna Mapping Functions (VMF) data [...] Read more.

An accurate estimation of zenith wet delay (ZWD) is crucial for global navigation satellite system (GNSS) positioning and GNSS-based precipitable water vapor (PWV) inversion. The forecast Vienna Mapping Function 3 (VMF3-FC) is a forecast product provided by the Vienna Mapping Functions (VMF) data server based on the European Centre for Medium-Range Weather Forecasts (ECMWF)-based numerical weather prediction (NWP) model. The VMF3-FC can provide ZWD at any time and for any location worldwide; however, it has an uneven accuracy distribution and fails to match the application requirements in certain areas. To address this issue, in this study, a calibrated model for VMF3-FC ZWD, named the XZWD model, was developed by utilizing observation data from 492 radiosonde sites globally from 2019–2021 and the eXtreme Gradient Boosting (XGBoost) algorithm. The performance of the XZWD model was validated using 2022 observation data from the 492 radiosonde sites. The XZWD model yields a mean bias of −0.03 cm and a root-mean-square error (RMSE) of 1.64 cm. The XZWD model outperforms the global pressure and temperature 3 (GPT3) model, reducing the bias and RMSE by 94.64% and 58.90%, respectively. Meanwhile, the XZWD model outperforms VMF3-FC, with a reduction of 92.68% and 6.29% in bias and RMSE, respectively. Furthermore, the XZWD model reduces the impact of ZWD accuracy by latitude, height, and seasonal variations more effectively than the GPT3 model and VMF3-FC. Therefore, the XZWD model yields higher stability and accuracy in global ZWD forecasting. Full article

(This article belongs to the Special Issue Applications of Satellite Geodesy in Hydrology, Glaciology, and Oceanography)

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