The DOI matches its Crossref record.
Estimating continental river basin discharges using multiple remote sensing data sets
Open accessAbstract
Rivers act as a source of fresh water for terrestrial life, yet the discharges are poorly documented since the existing direct observations are inadequate and some observation stations have been interrupted or discontinued. Discharge estimates using remote sensing thus have a great potential to supplement ground observations. There are remote sensing methods established to estimate discharge based on single parameter derived relationships; however, they are limited to specific sections due to their empirical nature. In this study, we propose an innovative method to estimate daily discharges for continental rivers (with river channel widths > 800 m (Birkett and Beckley, 2010)) using two satellite derived parameters. Multiple satellite altimetry data and Moderate Resolution Imaging Spectroradiometer (MODIS) data are used to provide a time series of river stages and effective river width. The derived MODIS and altimetry data are then used to optimize unknown parameters in a modified Manning's equation. In situ measurements are used to derive rating curves and to provide assessments of the estimated results. The Nash–Sutcliffe efficiency values for the estimates are between 0.60 and 0.97, indicating the power of the method and accuracy of the estimations. A comparison with a previously developed empirical multivariate equation for estimating river discharge shows that our method produces superior results, especially for large rivers. Furthermore, we found that discharge estimates using both effective river width and stage information consistently outperform those that only use stage data.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Response to Variations in River Flowrate by a Spaceborne GNSS-R River Width Estimator
Warnock, A., & Ruf, C. (2019) · Remote Sensing · doi:10.3390/rs11202450 · Cited by 25
Metadata
- Authors
- Sichangi, A. W., Wang, L., Yang, K., Chen, D., Wang, Z., Li, X., Zhou, J., Liu, W., & Kuria, D.
- Venue
- Remote Sensing of Environment
- Publisher
- Elsevier BV
- Published
- 2016-06
- Volume
- 179
- Pages
- 36-53
- ISSN
- 0034-4257 (ISSN-L)
- OpenAlex
- W2317026442
- Cited by
- 179
- References
- 0
- FWCI
- 8.10
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Estimating continental river basin discharges using multiple remote sensing data sets. https://app.cochat.ai/papers/10.1016/j.rse.2016.03.019
- The paper
- Sichangi, A. W., Wang, L., Yang, K., Chen, D., Wang, Z., Li, X., Zhou, J., Liu, W., & Kuria, D. (2016). Estimating continental river basin discharges using multiple remote sensing data sets. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2016.03.019
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.