سابِق|تال

Study progress in the river hydrologic remote sensing technique and its application on the Tibetan Plateau

  • role： First author第一作者
  • Affiliation：

    College of Surveying and Geotechnical Engineering, Henan Polytechnic University, Jiaozuo454000, China

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing210008, China

  • Email：shuqian0628@163.com
  • Introduction：刘沭岍，研究方向为河流水文遥感。E-mail： shuqian0628@163.com
LIU Shuqian12,  
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing210008, China

LIU Kai2,  
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing210008, China

ZENG Fanxuan2,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing210008, China

  • Email：cqsong@niglas.ac.cn
  • Introduction：宋春桥，研究方向为湖泊—流域水文遥感、资源环境遥感与全球变化。E-mail： cqsong@niglas.ac.cn
SONG Chunqiao2*

ملخص

Rivers are integral to the water cycle, underpinning human development, ecological health, and regional climate stability. Recently, global warming, glacial melt, and recurring hydrological disasters have intensified disturbances in river systems, necessitating broad-scale monitoring of complex hydrological changes. While traditional field measurements are valuable, limitations in their spatial and temporal coverage call for alternative approaches. With the advancement of sensor technology and the proliferation of satellite platforms, (satellite) remote sensing has emerged as a pivotal method for contemporary river hydrology monitoring. Compared with hydrological field measurements, it offers remarkable advantages in terms of real-time data acquisition, vast spatial coverage, and reduced economic costs. Various remote sensing monitoring techniques have been extensively applied to monitor river characteristics, such as area/width, water level fluctuations, runoff estimation, and forming diverse-scale remote sensing products of hydrological elements. This study reviews various monitoring techniques for river hydrological variables using optical or radar imaging and satellite altimetry. It analyzes the latest research progress in the hydrologic variables, encompassing river width, water area, water level, runoff, and their changes. Additionally, the spatial scale and feasibility of previous literature are thoroughly discussed. The Tibetan Plateau, known as the “Roof of the World,” is one of the regions with a serious shortage of in situ hydrological monitoring data, despite being the source of major rivers in Asia. The application of remote sensing technology for river hydrological monitoring on the Tibetan Plateau encounters challenges in data sharing, pronounced spatial and temporal heterogeneity of hydrological processes, and intricate response characteristics to a warming and humidification climate.This study begins by examining the main satellite remote sensing data sources and methods used to monitor various hydrological elements of rivers. It summarizes the current research progress in river hydrology monitoring using remote sensing technologies and explores future development opportunities. The review also addresses the advancements and challenges of hydrological remote sensing techniques specifically applied to river monitoring on the Tibetan Plateau. Several persistent issues in river hydrological remote sensing development have been identified: (1) The accuracy of extracting river area and width in regions with complex topography is severely affected by mixed pixels and spectral similarities. (2) In areas with sparse or no hydrological stations, assessing remote sensing data’s quality and potential applications remains challenging. (3) Comprehensive monitoring and studies on the spatial and temporal patterns of hydrological changes in the inland flow areas of the Tibetan Plateau are lacking. Future research directions for remote sensing of river hydrology are outlined as follows: (1) Multisource remote sensing data must be integrated, and the technologies and their applications must be enhanced for hydrological monitoring. (2) Universally applicable remote sensing algorithms for river hydrology must be optimized for innovation. These priorities aim to address the critical challenges in hydrological remote sensing and enhance the capability and accuracy of monitoring systems, particularly in complex and underserved regions, such as the Tibetan Plateau. This study aims to promote the deepening of river hydrology research on the Tibetan Plateau region, providing more accurate and scientific–technical support for practical water resource management and policy-making.

مفهوم

River;Hydrology;remote sensing;Tibetan Plateau;water extent;water level;runoff

References

  1. 1.
    Alijani Z, Lindsay J, Chabot M, Rowlandson T and Berg A. 2021. Sensitivity of C-band SAR polarimetric variables to the directionality of surface roughness parameters. Remote Sensing, 13(11): 2210
  2. 2.
    Allen G H and Pavelsky T M. 2015. Patterns of river width and surface area revealed by the satellite-derived North American River Width data set. Geophysical Research Letters, 42(2): 395-402
  3. 3.
    Allen G H and Pavelsky T M. 2018. Global extent of rivers and streams. Science, 361(6402): 585-588
  4. 4.
    Alsdorf D E, Rodríguez E and Lettenmaier D P. 2007. Measuring surface water from space. Reviews of Geophysics, 45(2): RG2002
  5. 5.
    Altenau E H, Pavelsky T M, Moller D, Pitcher L H, Bates P D, Durand M T and Smith L C. 2019. Temporal variations in river water surface elevation and slope captured by AirSWOT. Remote Sensing of Environment, 224: 304-316
  6. 6.
    Arnesen A S, Silva T S F, Hess L L, Novo E M L M, Rudorff C M, Chapman B D and McDonald K C. 2013. Monitoring flood extent in the lower Amazon River floodplain using ALOS/PALSAR ScanSAR images. Remote Sensing of Environment, 130: 51-61
  7. 7.
    Barnett T P, Adam J C and Lettenmaier D P. 2005. Potential impacts of a warming climate on water availability in snow-dominated regions. Nature, 438(7066): 303-309
  8. 8.
    Barton I J and Bathols J M. 1989. Monitoring floods with AVHRR. Remote Sensing of Environment, 30(1): 89-94
  9. 9.
    Birkett C M. 1998. Contribution of the TOPEX NASA radar altimeter to the global monitoring of large rivers and wetlands. Water Resources Research, 34(5): 1223-1239
  10. 10.
    Birkinshaw S. J., G. M. O’Donnell, P. Moore, C. G. Kilsby, H. J. Fowler, and P. A. M. Berry. 2010. Using satellite altimetry data to augment flow estimation techniques on the Mekong River. Hydrological Processes24 (26):3811-25.
  11. 11.
    Birkinshaw S. J., P. Moore, C. G. Kilsby, G. M. O’Donnell, A. J. Hardy, and P. A. M. Berry. 2014. Daily discharge estimation at ungauged river sites using remote sensing. Hydrological Processes28 (3):1043-54.
  12. 12.
    Bjerklie D M, Birkett C M, Jones J W, Carabajal C, Rover J A, Fulton J W and Garambois P A. 2018. Satellite remote sensing estimation of river discharge: application to the Yukon River Alaska. Journal of Hydrology, 561: 1000-1018
  13. 13.
    Bjerklie D M, Dingman S L, Vorosmarty C J, Bolster C H and Congalton R G. 2003. Evaluating the potential for measuring river discharge from space. Journal of Hydrology, 278(1/4): 17-38
  14. 14.
    Brakenridge G R, Cohen S, Kettner A J, De Groeve T, Nghiem S V, Syvitski J P M and Fekete B M. 2012. Calibration of satellite measurements of river discharge using a global hydrology model. Journal of Hydrology, 475: 123-136
  15. 15.
    Brisset P, Monnier J, Garambois P A and Roux H. 2018. On the assimilation of altimetric data in 1D Saint-Venant river flow models. Advances in Water Resources, 119: 41-59
  16. 16.
    Brivio P A, Colombo R, Maggi M and Tomasoni R. 2002. Integration of remote sensing data and GIS for accurate mapping of flooded areas. International Journal of Remote Sensing, 23(3): 429-441
  17. 17.
    Cao J T, Qin D H, Kang E S and Li Y Y. 2005. Chinese Science Bulletin, 50(21): 2403-2408
  18. 18.
    Chang F G, Li D X, Dong Z, Huang Y, He Z H and Chen X. 2021. Elevation spatial variation analysis and compensation in GEO SAR imaging. Remote Sensing, 13(10): 1888
  19. 19.
    Chen J, Chen J, Liao A P, Cao X, Chen L J, Chen X H, He C Y, Han G, Peng S, Lu M, Zhang W W, Tong X H and Mills J. 2015. Global land cover mapping at 30m resolution: a POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103: 7-27
  20. 20.
    Chen T, Song C Q, Zhan P F and Ma J S. 2022. How many Pan-Arctic lakes are observed by ICESat-2 in space and time?. Remote Sensing, 14(23): 5971
  21. 21.
    Chen Y T, Fok H S, Ma Z T and Tenzer R. 2019. Improved remotely sensed total basin discharge and its seasonal error characterization in the Yangtze River Basin. Sensors, 19(15): 3386
  22. 22.
    Coss S, Durand M, Yi Y C, Jia Y Y, Guo Q, Tuozzolo S, Shum C K, Allen G H, Calmant S and Pavelsky T. 2020. Global River Radar Altimetry Time Series (GRRATS): new river elevation earth science data records for the hydrologic community. Earth System Science Data, 12(1): 137-150
  23. 23.
    Cuo L, Zhang Y X, Zhu F X and Liang L Q. 2014. Characteristics and changes of streamflow on the Tibetan Plateau: a review. Journal of Hydrology: Regional Studies, 2: 49-68
  24. 24.
    Davis C H and Sun S. 2004. Long-Term thinning of the southeast greenland ice sheet from seasat, geosat, and GFO satellite radar altimetry. IEEE Geoscience and Remote Sensing Letters, 1(2): 47-50
  25. 25.
    Desai S D. 2002. Observing the pole tide with satellite altimetry. Journal of Geophysical Research: Oceans, 107(C11): 3186
  26. 26.
    Dhote P R, Thakur P K, Domeneghetti A, Chouksey A, Garg V, Aggarwal S P and Chauhan P. 2021. The use of SARAL/AltiKa altimeter measurements for multi-site hydrodynamic model validation and rating curves estimation: an application to Brahmaputra River. Advances in Space Research, 68(2): 691-702
  27. 27.
    Ding X W and Li X F. 2011. Monitoring of the water-area variations of Lake Dongting in China with ENVISAT ASAR images. International Journal of Applied Earth Observation and Geoinformation, 13(6): 894-901
  28. 28.
    Ding Y J, Ye B S, Han T D, Liu S Y, Shen Y P and Xie C W. 2007. Science in China (Series D:Earth Sciences), 37(2): 206-214
  29. 29.
    Dubey A K, Gupta P, Dutta S and Kumar B. 2014. Evaluation of satellite-altimetry-derived river stage variation for the braided Brahmaputra River. International Journal of Remote Sensing, 35(23): 7815-7827
  30. 30.
    Dubey A K, Gupta P, Dutta S and Singh R P. 2015a. Water level retrieval using SARAL/AltiKa observations in the braided brahmaputra river, eastern india. Marine Geodesy, 38(S1): 549-567
  31. 31.
    Dubey A K, Gupta P K, Dutta S and Singh R P. 2015a. An improved methodology to estimate river stage and discharge using Jason-2 satellite data. Journal of Hydrology, 529: 1776-1787
  32. 32.
    Durand M, Neal J, Rodríguez E, Andreadis K M, Smith L C and Yoon Y. 2014. Estimating reach-averaged discharge for the River Severn from measurements of river water surface elevation and slope. Journal of Hydrology, 511: 92-104
  33. 33.
    Durand M, Rodriguez E, Alsdorf D E and Trigg M. 2010. Estimating river depth from remote sensing swath interferometry measurements of river height, slope, and width. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(1): 20-31
  34. 34.
    Fan C B, Li J C, Wang D, Chu Y H, Xu X Y and Zou X C. 2005. Applications of ICESAT to geoscience research. Journal of Geodesy and Geodynamics, 25(2): 94-97
  35. 35.
    Fei J Y, Liu J T, Ke L H, Wang W, Wu P F and Zhou Y Y. 2022. A deep learning-based method for mapping alpine intermittent rivers and ephemeral streams of the Tibetan Plateau from Sentinel-1 time series and DEMs. Remote Sensing of Environment, 282: 113271
  36. 36.
    Feng M, Sexton J O, Channan S and Townshend J R. 2016. A global, high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm. International Journal of Digital Earth, 9(2): 113-133
  37. 37.
    Feng Y, Yang L K, Zhan P F, Luo S X, Chen T, Liu K and Song C Q. 2023. Synthesis of the ICESat/ICESat-2 and CryoSat-2 observations to reconstruct time series of lake level. International Journal of Digital Earth, 16(1): 183-209
  38. 38.
    Ferreira V, Gong Z, He X F, Zhang Y L and Andam-Akorful S. 2013. Estimating total discharge in the Yangtze River Basin using Satellite-Based observations. Remote Sensing, 5(7): 3415-3430
  39. 39.
    Feyisa G L, Meilby H, Fensholt R and Proud S R. 2014. Automated Water Extraction Index: a new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment, 140: 23-35
  40. 40.
    Fok H S and He Q. 2018. Water level reconstruction based on satellite gravimetry in the Yangtze River Basin. ISPRS International Journal of Geo-Information, 7(7): 286
  41. 41.
    Fok H S, Zhou L H and Ji H. 2020. Mekong delta runoff prediction using standardized Remotely-Sensed water balance variables. Water, 12(7): 2025
  42. 42.
    Getirana A C V. 2010. Integrating spatial altimetry data into the automatic calibration of hydrological models. Journal of Hydrology, 387(3/4): 244-255
  43. 43.
    Gleason C J and Durand M T. 2020. Remote sensing of river discharge: a review and a framing for the discipline. Remote Sensing, 12(7): 1107
  44. 44.
    Gleason C J and Smith L C. 2014. Toward global mapping of river discharge using satellite images and at-many-stations hydraulic geometry. Proceedings of the National Academy of Sciences of the United States of America, 111(13): 4788-4791
  45. 45.
    Gong P, Wang J, Yu L, Zhao Y C, Zhao Y Y, Liang L, Niu Z G, Huang X M, Fu H H, Liu S, Li C C, Li X Y, Fu W, Liu C X, Xu Y, Wang X Y, Cheng Q, Hu L Y, Yao W B, Zhang H, Zhu P, Zhao Z Y, Zhang H Y, Zheng Y M, Ji L Y, Zhang Y W, Chen H, Yan A, Guo J H, Yu L, Wang L, Liu X J, Shi T T, Zhu M H, Chen Y L, Yang G W, Tang P, Xu B, Giri C, Clinton N, Zhu Z L, Chen J and Chen J. 2013. Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data. International Journal of Remote Sensing, 34(7): 2607-2654
  46. 46.
    Guerreiro K, Fleury S, Zakharova E, Rémy F and Kouraev A. 2016. Potential for estimation of snow depth on Arctic sea ice from CryoSat-2 and SARAL/AltiKa missions. Remote Sensing of Environment, 186: 339-349
  47. 47.
    Hagemann M W, Gleason C J and Durand M T. 2017. BAM: Bayesian AMHG-manning inference of discharge using remotely sensed stream width, slope, and height. Water Resources Research, 53(11): 9692-9707
  48. 48.
    He Q, Fok H S, Chen Q and Chun K P. 2018. Water level reconstruction and prediction based on Space-Borne sensors: a case study in the mekong and Yangtze River Basins. Sensors, 18(9): 3076
  49. 49.
    Hong C, Wang M M and Zhu F Y. 2022. Water body extraction considering terrain and its application in Qinghai-Tibet Plateau. Geospatial Information, 20(7): 43-47
  50. 50.
    Huang C, Chen Y, Zhang S Q and Wu J P. 2018c. Detecting, extracting, and monitoring surface water from space using optical sensors: a review. Reviews of Geophysics, 56(2): 333-360
  51. 51.
    Huang Q, Long D, Du M D, Han Z Y and Han P F. 2020. Daily continuous river discharge estimation for ungauged basins using a hydrologic model calibrated by satellite altimetry: Implications for the SWOT mission. Water Resources Research, 56(7): e2020WR027309
  52. 52.
    Huang Q, Long D, Du M D, Zeng C, Li X D, Hou A Z and Hong Y. 2018a. An improved approach to monitoring Brahmaputra River water levels using retracked altimetry data. Remote Sensing of Environment, 211: 112-128
  53. 53.
    Huang Q, Long D, Du M D, Zeng C, Qiao G, Li X D, Hou A Z and Hong Y. 2018b. Discharge estimation in high-mountain regions with improved methods using multisource remote sensing: a case study of the Upper Brahmaputra River. Remote Sensing of Environment, 219: 115-134
  54. 54.
    Huang Q, Long D, Han Z Y and Han P F. 2022. High-resolution satellite images combined with hydrological modeling derive river discharge for headwaters: a step toward discharge estimation in ungauged basins. Remote Sensing of Environment, 277: 113030
  55. 55.
    Isikdogan F, Bovik A and Passalacqua P. 2015. Automatic channel network extraction from remotely sensed images by singularity analysis. IEEE Geoscience and Remote Sensing Letters, 12(11): 2218-2221
  56. 56.
    Isikdogan F, Bovik A and Passalacqua P. 2017b. RivaMap: an automated river analysis and mapping engine. Remote Sensing of Environment, 202: 88-97
  57. 57.
    Ke L H, Xu J H, Fan C Y, Liu K, Chen T, Wang S D, Chen X B, Song L J and Song C Q. 2022. Remote sensing reconstruction of long-term water level and storage variations of a poorly-gauged river in the Tibetan Plateau. Journal of Hydrology: Regional Studies, 40: 101020
  58. 58.
    Khanal S, Lutz A F, Kraaijenbrink P D A, van den Hurk B, Yao T and Immerzeel W W. 2021. Variable 21st century climate change response for rivers in high mountain asia at seasonal to decadal time scales. Water Resources Research, 57(5): e2020WR029266
  59. 59.
    Kittel C M M, Jiang L G, Tøttrup C and Bauer-Gottwein P. 2021. Sentinel-3 radar altimetry for river monitoring – a catchment-scale evaluation of satellite water surface elevation from Sentinel-3A and Sentinel-3B. Hydrology and Earth System Sciences, 25(1): 333-357
  60. 60.
    Klemenjak S, Waske B, Valero S and Chanussot J. 2012. Automatic detection of rivers in high-resolution SAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(5): 1364-1372
  61. 61.
    Koblinsky C J, Clarke R T, Brenner A C and Frey H. 1993. Measurement of river level variations with satellite altimetry. Water Resources Research, 29(6): 1839-1848
  62. 62.
    Kouraev A, Semovski S, Shimaraev M, Mognard N, Legresy B and Remy F. 2007. Observations of Lake Baikal ice from satellite altimetry and radiometry. Remote Sensing of Environment, 108(3): 240-253
  63. 63.
    Lao J Y, Wang C, Nie S, Xi X H and Wang J L. 2022. Monitoring and analysis of water level changes in Mekong River from ICESat-2 spaceborne laser altimetry. Water, 14(10): 1613
  64. 64.
    Leben R R, Hamlington B D and Haines B J. 2011. Seasat and geosat revisited: using sea level measurements to improve satellite altimeter orbits. The Journal of the Astronautical Sciences, 58(3): 479-493
  65. 65.
    Lei X, Ke L H, Yong B, Zhang J S, Cao Q Y. Evaluation of River Water Level Monitoring from Satellite Radar Altimetry Datasets over Chinese Rivers[J].Remote Sensing Technology and Application,2022, 37(1): 61-72.
  66. 66.
    Li D, Wang G, Qin C and Wu B S. 2021. River extraction under bankfull discharge conditions based on Sentinel-2 imagery and DEM data. Remote Sensing, 13(14): 2650
  67. 67.
    Li D, Wu B S, Chen B W, Qin C, Wang Y J, Zhang Y and Xue Y. 2020. Open-Surface river extraction based on sentinel-2 MSI imagery and DEM data: case study of the upper Yellow River. Remote Sensing, 12(17): 2737
  68. 68.
    Li D, Wu B S, Chen B W, Xue Y and Zhang Y. 2020. Review of water body information extraction based on satellite remote sensing. Journal of Tsinghua University (Science and Technology), 60(02):147-161.
  69. 69.
    Li D, Xue Y, Qin C, Wu B S, Chen B W and Wang G. 2022. A bankfull geometry dataset for major exorheic rivers on the Qinghai-Tibet Plateau. Scientific Data, 9(1): 498
  70. 70.
    Li F P, Zhang Y Q, Xu Z X, Teng J, Liu C M, Liu W F and Mpelasoka F. 2013. The impact of climate change on runoff in the southeastern Tibetan Plateau. Journal of Hydrology, 505: 188-201
  71. 71.
    Li H M, Bai J, Gan F P, Li X Q and Wang Z K. 2023. River discharge estimation based on remote sensing. Remote Sensing for Natural Resources, 35(2): 16 -24
  72. 72.
    Li J G, Huang S F and Li J R. 2010. Research on extraction of water body from ENVISAT ASAR images: a modified Otsu threshold method. Journal of Natural Disasters, 19(3): 139-145
  73. 73.
    Liang X G, Mao W, Yang K and Ji L Y. 2022. Automated small river mapping (ASRM) for the Qinghai-Tibet plateau based on sentinel-2 satellite imagery and MERIT DEM. Remote Sensing, 14(19): 4693
  74. 74.
    Liu G M, Schwartz F, Tseng K H, Shum C K and Lee S. 2018. Satellite altimetry for measuring river stages in remote regions. Environmental Earth Sciences, 77(18): 639
  75. 75.
    Liu G M, Schwartz F W, Tseng K H and Shum C K. 2015. Discharge and water-depth estimates for ungauged rivers: combining hydrologic, hydraulic, and inverse modeling with stage and water-area measurements from satellites. Water Resources Research, 51(8): 6017-6035
  76. 76.
    Luo S X, Song C Q, Zhan P F, Liu K, Chen T, Li W K and Ke L H. 2021. Refined estimation of lake water level and storage changes on the Tibetan Plateau from ICESat/ICESat-2. CATENA, 200: 105177
  77. 77.
    Luo Y, Wang X L, Piao S L, Sun L, Ciais P, Zhang Y Q, Ma C K, Gan R and He C S. 2018. Contrasting streamflow regimes induced by melting glaciers across the Tien Shan–Pamir–North Karakoram. Scientific Reports, 8(1): 16470
  78. 78.
    Lutz A F, Immerzeel W W, Kraaijenbrink P D A, Shrestha A B and Bierkens M F P. 2016. Climate change impacts on the upper indus hydrology: sources, shifts and extremes. PLoS ONE, 11(11): e0165630
  79. 79.
    Lutz A F, Immerzeel W W, Shrestha A B and Bierkens M F P. 2014. Consistent increase in High Asia’s runoff due to increasing glacier melt and precipitation. Nature Climate Change, 4(7): 587-592
  80. 80.
    Maheu C, Cazenave A and Mechoso C R. 2003. Water level fluctuations in the Plata basin (South America) from Topex/Poseidon Satellite Altimetry. Geophysical Research Letters, 30(3): 1143
  81. 81.
    Mao W, Yang K, Zhang W S, Wang Y H and Li M C. 2022. High-resolution global water body datasets underestimate the extent of small rivers. International Journal of Remote Sensing, 43(11): 4315-4330
  82. 82.
    Medina C, Gomez-Enri J, Alonso J J and Villares P. 2010. Water volume variations in Lake Izabal (Guatemala) from in situ measurements and ENVISAT Radar Altimeter (RA-2) and Advanced Synthetic Aperture Radar (ASAR) data products. Journal of Hydrology, 382(1/4): 34-48
  83. 83.
    Mengen D., M. Ottinger, P. Leinenkugel, and L. Ribbe. 2020. Modeling River Discharge Using Automated River Width Measurements Derived from Sentinel-1 Time Series. Remote Sensing12 (19).
  84. 84.
    Nielsen K, Zakharova E, Tarpanelli A, Andersen O B and Benveniste J. 2022. River levels from multi mission altimetry, a statistical approach. Remote Sensing of Environment, 270: 112876
  85. 85.
    Palmer M and Ruhi A. 2018. Measuring Earth’s rivers. Science, 361(6402): 546-547
  86. 86.
    Pan B T and Li J J. 1996. Qinghai-Tibetan Plateau: a driver and amplifier of the global climatic change——Ⅲ. the effects of the uplift of Qinghai-Tibetan Plateau on Climatic Changes. Journal of Lanzhou University (Natural Sciences), 32(1): 108-115
  87. 87.
    Papa F, Bala S K, Pandey R K, Durand F, Gopalakrishna V V, Rahman A and Rossow W B. 2012. Ganga-Brahmaputra river discharge from Jason-2 radar altimetry: an update to the long-term satellite-derived estimates of continental freshwater forcing flux into the Bay of Bengal. Journal of Geophysical Research: Oceans, 117(C11): C11021
  88. 88.
    Pavelsky T M and Smith L C. 2008. RivWidth: a software tool for the calculation of river widths from remotely sensed imagery. IEEE Geoscience and Remote Sensing Letters, 5(1): 70-73
  89. 89.
    Pekel J F, Cottam A, Gorelick N and Belward A S. 2016. High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633): 418-422
  90. 90.
    Pickens A H, Hansen M C, Hancher M, Stehman S V, Tyukavina A, Potapov P, Marroquin B and Sherani Z. 2020. Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series. Remote Sensing of Environment, 243: 111792
  91. 91.
    Pitcher L H, Pavelsky T M, Smith L C, Moller D K, Altenau E H, Allen G H, Lion C, Butman D, Cooley S W, Fayne J V and Bertram M. 2019. AirSWOT InSAR mapping of surface water elevations and hydraulic gradients across the Yukon Flats Basin, Alaska. Water Resources Research, 55(2): 937-953
  92. 92.
    Qin C, Wu B S, Wang G, Fu X D, Zhao L and Li D. 2022. Generalized hydraulic geometry and multi-frequency downstream hydraulic geometry of mountain rivers originated from the Qinghai-Tibet Plateau. Journal of Hydraulic Engineering, 53(2): 176-187
  93. 93.
    Quartly G D, Chen G, Nencioli F, Morrow R and Picot N. 2021. An overview of requirements, procedures and current advances in the Calibration/Validation of radar altimeters. Remote Sensing, 13(1): 125
  94. 94.
    Riggs R M, Allen G H, Wang J D, Pavelsky T M, Gleason C J, David C H and Durand M. 2023. Extending global river gauge records using satellite observations. Environmental Research Letters, 18(6): 064027
  95. 95.
    Santos Da Silva J, Calmant S, Seyler F, Rotunno Filho O C, Cochonneau G and Mansur W J. 2010. Water levels in the Amazon basin derived from the ERS 2 and ENVISAT radar altimetry missions. Remote Sensing of Environment, 114(10): 2160-2181
  96. 96.
    Schneider R, Godiksen P N, Villadsen H, Madsen H and Bauer-Gottwein P. 2017. Application of CryoSat-2 altimetry data for river analysis and modelling. Hydrology and Earth System Sciences, 21(2): 751-764
  97. 97.
    Schwatke C, Dettmering D, Bosch W and Seitz F. 2015. DAHITI – an innovative approach for estimating water level time series over inland waters using multi-mission satellite altimetry. Hydrology and Earth System Sciences, 19(10): 4345-4364
  98. 98.
    Shi Z L and Huang C. 2020. Recent advances in remote sensing of river characteristics. Progress in Geography, 39(4): 670-684
  99. 99.
    Sichangi A, Wang L and Hu Z D. 2018. Estimation of river discharge solely from remote-sensing derived data: an initial study over the Yangtze River. Remote Sensing, 10(9): 1385
  100. 100.
    Sichangi A W, Wang L, Yang K, Chen D L, Wang Z J, Li X P, Zhou J, Liu W B and Kuria D. 2016. Estimating continental river basin discharges using multiple remote sensing data sets. Remote Sensing of Environment, 179: 36-53
  101. 101.
    Smith L C, Isacks B L, Forster R R, Bloom A L and Preuss I. 1995. Estimation of discharge from braided glacial rivers using ERS 1 synthetic aperture radar: first results. Water Resources Research, 31(5): 1325-1329
  102. 102.
    Song C Q, Huang B and Ke L H. 2013. Modeling and analysis of lake water storage changes on the Tibetan Plateau using multi-mission satellite data. Remote Sensing of Environment, 135: 25-35
  103. 103.
    Song C Q, Huang B, Ke L H and Richards K S. 2014. Seasonal and abrupt changes in the water level of closed lakes on the Tibetan Plateau and implications for climate impacts. Journal of Hydrology, 514: 131-144
  104. 104.
    Su F G, Pritchard H D, Yao T D, Huang J H, Ou T H, Meng F C, Sun H, Li Y, Xu B Q, Zhu M L and Chen D L. 2022. Contrasting fate of western third pole’s water resources under 21st century climate change. Earth’s Future, 10(9): e2022EF002776
  105. 105.
    Su L F, Li Z X, Gao F and Yu M. 2021. A review of remote sensing image water extraction. Remote Sensing for Land and Resources, 33(1): 9-19
  106. 106.
    Sun W C, Fan J, Wang G Q, Ishidaira H, Bastola S, Yu J S, Fu Y H, Kiem A S, Zuo D P and Xu Z X. 2018. Calibrating a hydrological model in a regional river of the Qinghai–Tibet plateau using river water width determined from high spatial resolution satellite images. Remote Sensing of Environment, 214: 100-114
  107. 107.
    Tang Q H, Lan C, Su F G, Liu X C, Sun H, Ding J, Wang L, Leng G Y, Zhang Y Q, Sang Y F, Fang H Y, Zhang S F, Han D M, Liu X M, He L, Xu X M, Tang Y and Chen D L. 2019. Streamflow change on the Qinghai-Tibet Plateau and its impacts. Chinese Science Bulletin, 64(27): 2807-2821
  108. 108.
    Torres R, Snoeij P, Geudtner D, Bibby D, Davidson M, Attema E, Potin P, Rommen B, Floury N, Brown M, Traver I N, Deghaye P, Duesmann B, Rosich B, Miranda N, Bruno C, L’Abbate M, Croci R, Pietropaolo A, Huchler M and Rostan F. 2012. GMES Sentinel-1 mission. Remote Sensing of Environment, 120: 9-24
  109. 109.
    Tuozzolo S, Lind G, Overstreet B, Mangano J, Fonstad M, Hagemann M, Frasson R P M, Larnier K, Garambois P A, Monnier J and Durand M. 2019. Estimating river discharge with swath altimetry: a proof of concept using AirSWOT observations. Geophysical Research Letters, 46(3): 1459-1466
  110. 110.
    Villadsen H, Andersen O B, Stenseng L, Nielsen K and Knudsen P. 2015. CryoSat-2 altimetry for river level monitoring — Evaluation in the Ganges–Brahmaputra River basin. Remote Sensing of Environment, 168: 80-89
  111. 111.
    Wang D Z, Wang S M and Huang C. 2019. Comparison of Sentinel-2 imagery with Landsat8 imagery for surface water extraction using four common water indexes. Remote Sensing for Land and Resources, 31(3): 157-165
  112. 112.
    Wang L, Yao T D, Chai C H, Cuo L, Su F G, Zhang F, Yao Z J, Zhang Y S, Li X P, Qi J, Hu Z D, Liu J S and Wang Y W. 2021. TP-River: monitoring and quantifying total river runoff from the third pole. Bulletin of the American Meteorological Society, 102(5): E948-E965
  113. 113.
    Wang X B, Xie S P and Du J K. 2018. Water index formulation and its effectiveness research on the complicated surface water surroundings. Journal of Remote Sensing (in Chinese), 22(2): 360-372
  114. 114.
    Wang Z F, Li J B, Lin Y, Meng Y and Liu J G. 2022. GrabRiver: graph-theory-based river width extraction from remote sensing imagery. IEEE Geoscience and Remote Sensing Letters, 19: 1500505
  115. 115.
    Wu B F, Zhu W W, Zeng H N, Yan N N, Chang S and Zhao X F. 2020. Watershed remote sensing: definition and prospective. Advances in Water Science, 31(5): 654-673
  116. 116.
    Wu Q H, Ke L H, Wang J D, Pavelsky T M, Allen G H, Sheng Y W, Duan X J, Zhu Y Q, Wu J, Wang L, Liu K, Chen T, Zhang W S, Fan C Y, Yong B and Song C Q. 2023. Satellites reveal hotspots of global river extent change. Nature Communications, 14(1): 1587
  117. 117.
    Wu Z T, Snyder G, Vadnais C, Arora R, Babcock M, Stensaas G, Doucette P and Newman T. 2019. User needs for future Landsat missions. Remote Sensing of Environment, 231: 111214
  118. 118.
    Xiang J, Li H, Zhao J Y, Cai X B and Li P. 2021. Inland water level measurement from spaceborne laser altimetry: validation and comparison of three missions over the Great Lakes and lower Mississippi River. Journal of Hydrology, 597: 126312
  119. 119.
    Xu Y S, Gao L and Zhang Y H. 2017. New generation altimetry satellite SWOT and its reference to China’s swath altimetrysatellitel. Remote Sensing Technology and Application, 32(1): 84-94
  120. 120.
    Xue Y, Qin C, Wu B S, Li D and Fu X D. 2023. Automatic extraction of mountain river information from multiple Chinese high-resolution remote sensing satellite images. Journal of Tsinghua University (Science and Technology), 63(1): 134-145
  121. 121.
    Yamazaki D, O’Loughlin F, Trigg M A, Miller Z F, Pavelsky T M and Bates P D. 2014. Development of the global width database for large rivers. Water Resources Research, 50(4): 3467-3480
  122. 122.
    Yamazaki D, Trigg M A and Ikeshima D. 2015. Development of a global ~90 m water body map using multi-temporal Landsat images. Remote Sensing of Environment, 171: 337-351
  123. 123.
    Yang J, Huang X and Tang Q H. 2020. Satellite-derived river width and its spatiotemporal patterns in China during1990年—2015. Remote Sensing of Environment, 247: 111918
  124. 124.
    Yang Y C, Li B Y, Yin Z S and Zhang Q S. 1982. The formation and evolution of landforms in the Xizang Plateau. Acta Geographica Sinica, 37(1): 76-87
  125. 125.
    Ye B S, Ding Y J, Jiao K Q, Shen Y P and Zhang J. 2012. The response of river discharge to climate warming in cold region over China. Quaternary Sciences, 32(1): 103-110
  126. 126.
    Zhang F, Zhu X L and Liu D S. 2014. Blending MODIS and Landsat images for urban flood mapping. International Journal of Remote Sensing, 35(9): 3237-3253
  127. 127.
    Zhang J Y, Liu J F, Jin J L, Ma T, Wang G Q, Liu H W, Min X, Wang F, Lin J, Bao Z X and Liu C S. 2019. Evolution and trend of water resources in Qinghai-Tibet Plateau. Bulletin of Chinese Academy of Sciences, 34(11): 1264-1273
  128. 128.
    Zhang S T, Lu X, Lu Y, Cheng L, Li M C and Yang K. 2021. Tracking dynamic river networks in the Tibetan Plateau with high-resolution CubeSat imagery. National Remote Sensing Bulletin, 25(10): 2142-2152
  129. 129.
    Zhang Y, Zhang G Q and Zhu T T. 2020. Seasonal cycles of lakes on the Tibetan Plateau detected by Sentinel-1 SAR data. Science of the Total Environment, 703: 135563
  130. 130.
    Zheng J J, Ke C Q, Shao Z D and Li F. 2016. Monitoring changes in the water volume of Hulun Lake by integrating satellite altimetry data and Landsat images between 1992 and 2010. Journal of Applied Remote Sensing, 10(1): 016029

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