{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T07:59:37Z","timestamp":1777363177587,"version":"3.51.4"},"reference-count":38,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2020,5,18]],"date-time":"2020-05-18T00:00:00Z","timestamp":1589760000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this study, a new approach for estimating volume variations of lakes and reservoirs using water levels from satellite altimetry and surface areas from optical imagery is presented. Both input data sets, namely water level time series and surface area time series, are provided by the Database of Hydrological Time Series of Inland Waters (DAHITI), developed and maintained by the Deutsches Geod\u00e4tisches Forschungsinsitut der Technischen Universit\u00e4t M\u00fcnchen (DGFI-TUM). The approach is divided into three parts. In the first part, a hypsometry model based on the new modified Strahler approach is computed by combining water levels and surface areas. The hypsometry model describes the dependency between water levels and surface areas of lakes and reservoirs. In the second part, a bathymetry between minimum and maximum surface area is computed. For this purpose, DAHITI land-water masks are stacked using water levels derived from the hypsometry model. Finally, water levels and surface areas are intersected with the bathymetry to estimate a time series of volume variations in relation to the minimum observed surface area. The results are validated with volume time series derived from in-situ water levels in combination with bathymetric surveys. In this study, 28 lakes and reservoirs located in Texas are investigated. The absolute volumes of the investigated lakes and reservoirs vary between 0.062 km     3     and 6.041 km     3    . The correlation coefficients of the resulting volume variation time series with validation data vary between 0.80 and 0.99. Overall, the relative errors with respect to volume variations vary between 2.8% and 14.9% with an average of 8.3% for all 28 investigated lakes and reservoirs. When comparing the resulting RMSE with absolute volumes, the absolute errors vary between 1.5% and 6.4% with an average of 3.1%. This study shows that volume variations can be calculated with a high accuracy which depends essentially on the quality of the used water levels and surface areas. In addition, this study provides a hypsometry model, high-resolution bathymetry and water level time series derived from surface areas based on the hypsometry model. All data sets are publicly available on the Database of Hydrological Time Series of Inland Waters.<\/jats:p>","DOI":"10.3390\/rs12101606","type":"journal-article","created":{"date-parts":[[2020,5,18]],"date-time":"2020-05-18T11:34:14Z","timestamp":1589801654000},"page":"1606","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Volume Variations of Small Inland Water Bodies from a Combination of Satellite Altimetry and Optical Imagery"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4741-3449","authenticated-orcid":false,"given":"Christian","family":"Schwatke","sequence":"first","affiliation":[{"name":"Deutsches Geod\u00e4tisches Forschungsinsitut der Technischen Universit\u00e4t M\u00fcnchen (DGFI-TUM), Arcisstra\u00dfe 21, 80333 M\u00fcnchen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8940-4639","authenticated-orcid":false,"given":"Denise","family":"Dettmering","sequence":"additional","affiliation":[{"name":"Deutsches Geod\u00e4tisches Forschungsinsitut der Technischen Universit\u00e4t M\u00fcnchen (DGFI-TUM), Arcisstra\u00dfe 21, 80333 M\u00fcnchen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0718-6069","authenticated-orcid":false,"given":"Florian","family":"Seitz","sequence":"additional","affiliation":[{"name":"Deutsches Geod\u00e4tisches Forschungsinsitut der Technischen Universit\u00e4t M\u00fcnchen (DGFI-TUM), Arcisstra\u00dfe 21, 80333 M\u00fcnchen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2022","DOI":"10.1073\/pnas.1717312115","article-title":"Climate-change\u2013driven accelerated sea-level rise detected in the altimeter era","volume":"115","author":"Nerem","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1175\/1520-0442(2002)015<0353:TIVOSS>2.0.CO;2","article-title":"Toward Improved Validation of Satellite Sea Surface Skin Temperature Measurements for Climate Research","volume":"15","author":"Donlon","year":"2002","journal-title":"J. Clim."},{"key":"ref_3","unstructured":"Gleick, P. (1993). World Fresh Water Resources. Water in Crisis\u2014A Guide to the World\u2019s Fresh Water Resources, Oxford University Press. Chapter 2."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Brauch, H.G., Oswald Spring, U., Mesjasz, C., Grin, J., Kameri-Mbote, P., Chourou, B., Dunay, P., and Birkmann, J. (2011). Quantifying Global Environmental Change Impacts: Methods, Criteria and Definitions for Compiling Data on Hydro-meteorological Disasters. Coping with Global Environmental Change, Disasters and Security: Threats, Challenges, Vulnerabilities and Risks, Springer.","DOI":"10.1007\/978-3-642-17776-7"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Hasan, M., Moody, A., Benninger, L., and Hedlund, H. (2018). How war, drought, and dam management impact water supply in the Tigris and Euphrates Rivers. Ambio.","DOI":"10.1007\/s13280-018-1073-4"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"121154","DOI":"10.1016\/j.jclepro.2020.121154","article-title":"Historical assessment and future sustainability challenges of Egyptian water resources management","volume":"263","author":"Luo","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_7","unstructured":"(2019, January 25). Global Runoff Data Center: Statistics Based on GRDC Station Catalogue from 19 October 2018. Available online: https:\/\/www.bafg.de\/SharedDocs\/ExterneLinks\/GRDC\/grdc_stations_ftp.html?nn=201352."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.proenv.2010.09.008","article-title":"Needs for Climate Information in Support of Decision-Making in the Water Sector","volume":"1","author":"Stakhiv","year":"2010","journal-title":"Procedia Environ. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/S0022-1694(02)00283-4","article-title":"A global hydrological model for deriving water availability indicators: Model tuning and validation","volume":"270","author":"Kaspar","year":"2003","journal-title":"J. Hydrol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1007\/s00190-016-0892-y","article-title":"A systematic impact assessment of GRACE error correlation on data assimilation in hydrological models","volume":"90","author":"Schumacher","year":"2016","journal-title":"J. Geod."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Frappart, F., and Ramillien, G. (2018). Monitoring Groundwater Storage Changes Using the Gravity Recovery and Climate Experiment (GRACE) Satellite Mission: A Review. Remote Sens., 10.","DOI":"10.3390\/rs10060829"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ni, S., Chen, J., Wilson, C.R., and Hu, X. (2017). Long-Term Water Storage Changes of Lake Volta from GRACE and Satellite Altimetry and Connections with Regional Climate. Remote Sens., 9.","DOI":"10.3390\/rs9080842"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.rse.2012.01.001","article-title":"Inter-annual water storage changes in the Aral Sea from multi-mission satellite altimetry, optical remote sensing, and GRACE satellite gravimetry","volume":"123","author":"Singh","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1502","DOI":"10.1109\/JSTARS.2013.2258326","article-title":"Application of Multi-Sensor Satellite Data to Observe Water Storage Variations","volume":"6","author":"Singh","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"25179","DOI":"10.1029\/95JC02125","article-title":"The contribution of TOPEX\/POSEIDON to the global monitoring of climatically sensitive lakes","volume":"100","author":"Birkett","year":"1995","journal-title":"J. Geophys. Res. Oceans"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1098","DOI":"10.1016\/j.crte.2006.08.002","article-title":"Lake studies from satellite radar altimetry","volume":"338","author":"Birkett","year":"2006","journal-title":"Comptes Rendus Geosci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1497","DOI":"10.1016\/j.asr.2011.01.004","article-title":"SOLS: A lake database to monitor in the Near Real Time water level and storage variations from remote sensing data","volume":"47","author":"Jelinski","year":"2011","journal-title":"Adv. Space Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4345","DOI":"10.5194\/hess-19-4345-2015","article-title":"DAHITI - an innovative approach for estimating water level time series over inland waters using multi-mission satellite altimetry","volume":"19","author":"Schwatke","year":"2015","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1080\/01490419.2015.1008710","article-title":"Potential of SARAL\/AltiKa for Inland Water Applications","volume":"38","author":"Schwatke","year":"2015","journal-title":"Marine Geod."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1080\/01431160701294679","article-title":"Monitoring geomorphologic changes using Landsat TM and ETM+ data in the Hendijan River delta, southwest Iran","volume":"29","author":"Ghanavati","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"DeVries, B., Huang, C., Lang, M.W., Jones, J.W., Huang, W., Creed, I.F., and Carroll, M.L. (2017). Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9080807"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Schwatke, C., Scherer, D., and Dettmering, D. (2019). Automated Extraction of Consistent Time-Variable Water Surfaces of Lakes and Reservoirs Based on Landsat and Sentinel-2. Remote Sens., 11.","DOI":"10.3390\/rs11091010"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.rse.2014.12.004","article-title":"Retrieval of nearshore bathymetry from Landsat 8 images: A tool for coastal monitoring in shallow waters","volume":"159","author":"Pacheco","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Singh, A., Kumar, U., and Seitz, F. (2016). Correction: Singh, A., et al. Remote Sensing of Storage Fluctuations of Poorly Gauged Reservoirs and State Space Model (SSM)-Based Estimation. Remote Sens., 8.","DOI":"10.3390\/rs8110960"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"7538","DOI":"10.1364\/AO.58.007538","article-title":"Rapid estimation of bathymetry from multispectral imagery without in situ bathymetry data","volume":"58","author":"Liu","year":"2019","journal-title":"Appl. Opt."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhu, S., Liu, B., Wan, W., Xie, H., Fang, Y., Chen, X., Li, H., Fang, W., Zhang, G., and Tao, M. (2019). A New Digital Lake Bathymetry Model Using the Step-Wise Water Recession Method to Generate 3D Lake Bathymetric Maps Based on DEMs. Water, 11.","DOI":"10.3390\/w11061151"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.rse.2013.03.010","article-title":"Estimating water volume variations in lakes and reservoirs from four operational satellite altimetry databases and satellite imagery data","volume":"134","author":"Duan","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Gao, H., Birkett, C., and Lettenmaier, D.P. (2012). Global monitoring of large reservoir storage from satellite remote sensing. Water Resour. Res., 48.","DOI":"10.1029\/2012WR012063"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"407","DOI":"10.3390\/rs6010407","article-title":"Remote Sensing-Derived Bathymetry of Lake Poop\u00f3","volume":"6","author":"Arsen","year":"2014","journal-title":"Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1007\/s10712-016-9362-6","article-title":"Lake Volume Monitoring from Space","volume":"37","author":"Arsen","year":"2016","journal-title":"Surv. Geophys."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"669","DOI":"10.5194\/hess-23-669-2019","article-title":"A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry","volume":"23","author":"Busker","year":"2019","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2255","DOI":"10.3390\/rs6032255","article-title":"Multi-Mission Cross-Calibration of Satellite Altimeters: Constructing a Long-Term Data Record for Global and Regional Sea Level Change Studies","volume":"6","author":"Bosch","year":"2014","journal-title":"Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1130\/0016-7606(1952)63[1117:HAAOET]2.0.CO;2","article-title":"Hypsometric (Area-Altitude) Analysis of Erosional topography","volume":"63","author":"Strahler","year":"1952","journal-title":"GSA Bull."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1002\/2016GL071378","article-title":"The volume and mean depth of Earth\u2019s lakes","volume":"44","author":"Cael","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"ref_35","unstructured":"Texas Water Development Board (2010). Volumetric and Sedimentation Survey of Ray Roberts Lake (September\u2013October 2008 Survey)."},{"key":"ref_36","unstructured":"Texas Water Development Board (2018). Volumetric Survey of Hubbard Creek Reservoir (March 2017\u2013January 2018 Survey)."},{"key":"ref_37","unstructured":"Texas Water Development Board (2014). Volumetric and Sedimentation Survey of Lake Palestine (July\u2013August 2012 Survey)."},{"key":"ref_38","unstructured":"Texas Water Development Board (2005). Volumetric Survey of Lake Palestine (June 2003 Survey)."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/10\/1606\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:29:49Z","timestamp":1760174989000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/10\/1606"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,18]]},"references-count":38,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["rs12101606"],"URL":"https:\/\/doi.org\/10.3390\/rs12101606","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,18]]}}}