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RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery
Open accessAbstract
The wetted width of a river is one of the most important hydraulic parameters that can be readily measured using remote sensing. Remotely sensed river widths are used to estimate key attributes of river systems, including changes in their surface area, channel storage, and discharge. Although several published algorithms automate river network and width extraction from remote sensing images, they are limited by only being able to run on local computers and do not automatically manage cloudy images as input. Here we present RivWidthCloud, a river width software package developed on the Google Earth Engine cloud computing platform. RivWidthCloud automatically extracts river centerline and widths from optical satellite images with the ability to flag observations that are obstructed by features like clouds, cloud shadows, and snow based on existing quality band classification. Because RivWidthCloud is built on a popular cloud computing platform, it allows users to easily apply the algorithm to the platform’s vast archive of remote sensing images, thereby reducing the users’ overhead for computing hardware and data storage. By comparing RivWidthCloud-derived widths from Landsat images to in situ widths from the U.S. and Canada, we show that RivWidthCloud can estimate widths with high accuracy (root mean square error: 99 m; mean absolute error: 43 m; mean bias: −21 m). By making RivWidthCloud publicly available, we anticipate that it will be used to address both river science questions and operational applications of water resource management.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
RivWidthCloud is a Google Earth Engine software package that automatically extracts river centerlines and widths from optical satellite imagery, including partially cloudy images, by adapting RivWidth and adding quality flags. Validation against same-day in situ widths from U.S. and Canada Landsat data shows high accuracy (RMSE 99.2 m; MAE 43.1 m; mean bias −20 m).
Key findings
- RivWidthCloud extracts river centerlines and widths in three main steps: river mask, centerline, and width measurement; it uses GRWL to distinguish river pixels from nonriver water, checks connectivity to GRWL centerlines within a default 4-km radius, and removes islands smaller than a default ∼0.3 km².
- It provides quality flags for cloud, cloud shadow, snow/ice, and topographic shadow; a nonzero flag suggests the width measurement may be impacted by that condition.
- Validation used 1514 valid flag-free widths from Landsat images with same-day in situ measurements for 1984–2018, across the U.S. (492 stations with 1476 widths) and Canada (27 stations with 38 widths); RMSE was 99.2 m, MAE 43.1 m, and mean bias −20 m.
- Unlike RivWidth, RivWidthCloud preserves each channel’s own centerline and measures each channel’s width separately rather than outputting a single centerline with total flow width summed across all channels.
Methods
- Design: a Google Earth Engine implementation of RivWidth that works in principle with any remote sensing product providing water and obstruction classifications, but is described and validated using Landsat TM, ETM+, and OLI imagery.
- River mask: spectral water classification with reported ∼97% accuracy using MNDWI, NDVI, and EVI thresholds; channel mask is all water pixels connected to GRWL river centerlines within a default 4-km radius; islands below ∼0.3 km² are removed.
- Centerline and width: distance transform, gradient convolution, thresholding gradient values <0.9, and skeletonization produce a 1-pixel-wide centerline; spurious branches are trimmed by removing 500 pixels (∼1500 m for Landsat) or the segment length to the next confluence, whichever is shorter; widths are calculated along orthogonal directions using a 9 × 9-pixel kernel and w = w_m × u. Validation compares Landsat-derived widths to USGS and Water Survey of Canada same-day in situ widths.
Limitations
- Stated: default centerlines do not guarantee connectivity at confluences; connectivity can be enforced by calculating the centerline from the river mask via skeletonization only, but this is slow and could exceed available GEE computational resources over large areas.
- Stated: width accuracy depends on water classification and on cloud/shadow/snow/ice flagging accuracy; the error assessment is likely conservative because it does not account for in situ measurement errors or mismatches between in situ and satellite measurement locations where width changes rapidly.
- Evident from the text: validation is limited to U.S. and Canada gauging stations and Landsat imagery, and RivWidthCloud is currently implemented only for Landsat products, though the authors state it can be expanded to other products such as Sentinel-2 or MODIS.
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In the paper
“For the period 1984-2018, we obtained 1514 valid (flag-free) widths from Landsat images that had same-day in situ measurements at gauging stations, across the U.S. (492 stations with 1476 widths) and Canada (27 stations with 38 widths).”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > III. RESULTS AND DISCUSSION
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“By comparing RivWidthCloudderived widths from Landsat images to in situ widths from the U.S. and Canada, we show that RivWidthCloud can estimate widths with high accuracy (root mean square error: 99 m; mean absolute error: 43 m; mean bias: -21 m).”
- Also stated in: § III. RESULTS AND DISCUSSION · plain
“We found that the widths computed using RivWidthCloud on Landsat imagery (w L ) closely matched the in situ measurements (w G ) (root mean square error = 99.2m; mean absolute error = 43.1 m; mean bias = -20 m) (see also Fig. 3).”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > Abstract
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“The resulting 1-pixel-wide centerlines often exhibit spurious branches-artifacts mainly produced by irregular bank shapes.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > B. Delineating the River Centerline
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“The centerlines from RivWidthCloud by default do not guarantee connectivity at confluences.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > III. RESULTS AND DISCUSSION
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“However, the resulting orthogonal direction is only accurate when exactly two pixels intersect the rim of the kernel, one from each end of the overlapping centerline segment (Fig. 2).”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > C. Calculating River Width
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“Specifically, with each width measurement, RivWidthCloud provides several quality flags indicating the potential influence of each of these features”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > D. Assigning Quality Flags
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“This lack of connectivity is a necessary side-effect of our default method of calculating gradient to accelerate the centerline computation.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > III. RESULTS AND DISCUSSION
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“and therefore a river width algorithm that can automatically run on images with cloud cover will vastly increase the amount of data available for river monitoring purposes.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > I. INTRODUCTION
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“By adapting the widely used RivWidth algorithm [13] to the GEE platform [16], this new tool contains several novel features that allow for rapid river width extraction from partially cloud-covered images.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > I. INTRODUCTION
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“Moreover, the recently discovered at-many-stations hydraulic geometry (AMHG) allows for estimating river discharge using widths alone [7].”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > I. INTRODUCTION
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“In both situations, the in situ widths did not serve as good validation data (validation can be assessed for each individual station using the RivWidth-Cloud GEE app: https://eeproject.users.earthengine.app/view/rivwidthcloud-validation).”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > III. RESULTS AND DISCUSSION
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“and we expand on this ability by allowing such processing automatically in GEE, which allows easy global application.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > D. Assigning Quality Flags
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“While in situ gauging stations measure river water level (height) to estimate discharge, the numbers of stations providing data to the public have declined since the 1980s [5], due to both decreased monitoring budgets and changes in international data sharing policies.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > I. INTRODUCTION
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“These advantages allow users to quickly extract typical width statistics and time series without the overhead of downloading, storing, and processing remote sensing data locally.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > IV. CONCLUSION
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“To classify surface water [Fig.1(b)], RivWidthCloud uses a simple, spectral-based water classification formula with a reported accuracy of ∼97% [18].”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > A. Developing the River Mask
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“It is likely that the error assessment included here is conservative because it does not factor in errors in in situ width measurements or mismatches between in situ and satellite measurement locations in areas where width changes rapidly (see Fig. S1).”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > IV. CONCLUSION
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“GRWL contains width values associated with each centerline location, derived from Landsat images using RivWidth, for rivers wider than 30 m globally.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > A. Developing the River Mask
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“Moreover, repeated width measurements can be used to estimate river discharge based on classic hydraulic geometry relationships [4].”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > I. INTRODUCTION
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“This approach differs substantially from that of the RivWidth algorithm [13], which outputs a single centerline with total flow width summed across all channels.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > B. Delineating the River Centerline
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“Because of its accurate cloud classification algorithm [17] and its long mission history, we describe and validate RivWidthCloud using data from the Landsat program, specifically imagery from the Thematic Mapper (TM), Enhanced TM Plus, and Operational Land Imager instruments.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > II. METHOD
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“The values of the centerline pixels from the convolved image, when divided by two, approximate their orthogonal directions.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > C. Calculating River Width
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In the paper
“Here, we present RivWidthCloud, the first Google Earth Engine (GEE)-based river width algorithm that automatically extracts river centerlines and widths from remotely sensed images (cloud-free or not) with minimal user input and minimal required computation hardware.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > I. INTRODUCTION
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- Rests on an assumption: Many satellite-based river discharge estimation methods that use river observations require river width as input.
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“The resulting final centerline [Fig.1(h)] preserves the river's channel structure such that each channel retains its own centerline, and as a result, each channel's width is measured separately.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > B. Delineating the River Centerline
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In the paper
“Large inconsistencies between widths obtained in situ and remotely were mainly from stations where: 1) nearby river widths changed rapidly or 2) the in situ width was taken a certain distance upstream or downstream from the gaging station.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > III. RESULTS AND DISCUSSION
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“RivWidthCloud is currently implemented with remote sensing products from Landsat program, given its long mission history, wide application, and sophisticated cloud algorithm.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > IV. CONCLUSION
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In the paper
“Two distinct advantages of RivWidthCloud over previous river width measuring algorithms [1], [2], [9]- [14] are its ease of use based on a popular cloud computing environment and its flagging capability to automatically reduce adverse impact from features like cloud and shadows.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > IV. CONCLUSION
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In the paper
“For the majority of the width validation pairs, the accuracy of Landsat-derived widths depends on the accuracy of the image classification-the classification for water determines the extent of the river while the classification of cloud/shadow/snow/ice determines the flagging accuracy.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > III. RESULTS AND DISCUSSION
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“V ARIATIONS in centerline and width along river channels have been used to infer the morphodynamics of rivers and river deltas [1]- [3].”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > I. INTRODUCTION
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In the paper
“We note that the accuracy of the channel/river mask and the widths derived from them is highly dependent on the accuracy of the inundation map, and hence on the water classification method that is used to generate the inundation map.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > A. Developing the River Mask
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In the paper
“Because RivWidthCloud is built on a popular cloud computing platform, it allows users to easily apply the algorithm to the platform's vast archive of remote sensing images, thereby reducing the users' overhead for computing hardware and data storage.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > Abstract
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“Recent work has pioneered the ability to distinguish these features using a machine learning approach [25], [26]”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > D. Assigning Quality Flags
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In the paper
“However, solely relying on skeletonization to calculate the centerline is slow and could potentially exceed the computational resources available to a GEE user when applied over a large area.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > III. RESULTS AND DISCUSSION
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“At any given time, clouds cover ∼67% of the earth's surface [15]”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > I. INTRODUCTION
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“For example, point-scale and reach-scale [6] multitemporal river widths have been used to estimate river discharge using hydraulic geometry relationships established with in situ discharge measurements.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > I. INTRODUCTION
What the sources say
- Not matchedcites [6]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“RivWidthCloud in principle works with any remote sensing product capable of providing classifications of water and visual obstructions (cloud, snow/ice, etc.).”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > II. METHOD
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The methods for classifying water and the accuracy of such classifications vary among remote sensing products.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, a flag with a nonzero value suggests the width measurement may be impacted by the flagged condition.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > D. Assigning Quality Flags
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“In combination with the increasing number of earth-observing satellite missions, including the forthcoming Surface Water and Ocean Topography (SWOT) mission [8], satellite-derived widths will help us observe ungauged rivers and supplement in situ gauging networks, ensuring greater monitoring consistency globally.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > I. INTRODUCTION
What the sources say
- Not matchedcites [8]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, the range of remote sensing data for RivWidthCloud can be readily expanded to include other remote sensing data products (e.g., from Sentinel 2 or Moderate Resolution Imaging Spectroradiometer).”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > IV. CONCLUSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The accuracy of RivWidthCloud's width measurements depends on the accuracy of the water classification, which can be impacted by clouds, cloud shadows, topographic shadows, and the presence of snow/ice.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > D. Assigning Quality Flags
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Most existing algorithms to extract river widths are designed for optical images, and their application is heavily limited by the presence of clouds, as they require cloud-free images as input [1], [2], [9]- [14].”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > I. INTRODUCTION
What the sources say
- Not matchedcites [1]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [9]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [10]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [11]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [12]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [13]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [14]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While the maximum distance scales with river size, the gradient close to the centerline is nearly always a local minimum and close to zero [Fig.1(f)].”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > B. Delineating the River Centerline
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It also allows for the generation of spatially continuous multitemporal width data that can be used to provide a priori data set of variations of river location and width for future satellite missions like SWOT, or to estimate river discharge either via AMHG alone or via in conjunction with other remote sensing data products.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > IV. CONCLUSION
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In cases where connectivity is needed, it can be enforced by directly calculating the centerline from the river mask via skeletonization only, skipping the steps of distance transform and gradient.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > III. RESULTS AND DISCUSSION
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“RivWidthCloud automatically extracts river centerline and widths from optical satellite images with the ability to flag observations that are obstructed by features like clouds, cloud shadows, and snow based on existing quality band classification.”
- Also stated in: § I. INTRODUCTION · plain
“Here, we present RivWidthCloud, the first Google Earth Engine (GEE)-based river width algorithm that automatically extracts river centerlines and widths from remotely sensed images (cloud-free or not) with minimal user input and minimal required computation hardware.”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § I. INTRODUCTION · plain
The paper relies on this without support.
Many satellite-based river discharge estimation methods that use river observations require river width as input.
“Among various satellite-based river discharge estimation methods that use river observations, many require river width as input.”
The paper relies on this without support.
Landsat has a long mission history.
“Because of its accurate cloud classification algorithm [17] and its long mission history, we describe and validate RivWidthCloud using data from the Landsat program, specifically imagery from the Thematic Mapper (TM), Enhanced TM Plus, and Operational Land Imager instruments.”
The paper relies on this without support.
The methods for classifying water and the accuracy of such classifications vary among remote sensing products.
“The methods for classifying water and the accuracy of such classifications vary among remote sensing products.”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > D. Assigning Quality Flags
“The Multi-Error-Removed Improved-Terrain DEM (MERIT DEM) [23]”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > References
“23. J. W. Jones, "Improved automated detection of subpixel-scale inundation-Revised dynamic surface water extent (DSWE) par- tial surface water tests,"”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
“removing either 500 pixels (∼1500 m for Landsat)”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > B. Delineating the River Centerline
Reported 1500 · recomputed 15000
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > Abstract
“mean bias: -21 m”
§ RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery > III. RESULTS AND DISCUSSION
“mean bias = -20 m”
Citations
Cites
Google Earth Engine: Planetary-scale geospatial analysis for everyone
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.06.031 · Cited by 16,325
Overview of the radiometric and biophysical performance of the MODIS vegetation indices
HUETE, A. R., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002) · Remote Sensing of Environment · doi:10.1016/s0034-4257(02)00096-2 · Cited by 9,954
Xu, H. (2006) · International Journal of Remote Sensing · doi:10.1080/01431160600589179 · Cited by 6,446
The hydraulic geometry of stream channels and some physiographic implications
Leopold, L. B., & Maddock, T. (1953) · Professional Paper · doi:10.3133/pp252 · Cited by 2,966
Zhu, Z., Wang, S., & Woodcock, C. E. (2015) · Remote Sensing of Environment · doi:10.1016/j.rse.2014.12.014 · Cited by 1,650
Global extent of rivers and streams
Allen, G. H., & Pavelsky, T. M. (2018) · Science · doi:10.1126/science.aat0636 · Cited by 913
Self-citationSpatial and Temporal Distribution of Clouds Observed by MODIS Onboard the Terra and Aqua Satellites
King, M. D., Platnick, S. E., Menzel, W. P., Ackerman, S. A., & Hubanks, P. A. (2013) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2012.2227333 · Cited by 847
Analysis of the dynamics of African vegetation using the normalized difference vegetation index
Townshend, J. R. G., & Justice, C. (1986) · International Journal of Remote Sensing · doi:10.1080/01431168608948946 · Cited by 543
The SWOT Mission and Its Capabilities for Land Hydrology
Biancamaria, S., Lettenmaier, D. P., & Pavelsky, T. M. (2016) · Surveys in Geophysics · doi:10.1007/s10712-015-9346-y · Cited by 458
Self-citationGleason, C. J., & Smith, L. C. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1317606111 · Cited by 369
Cited by
Sorted by citations
Recent changes to Arctic river discharge
Feng, D., Gleason, C. J., Lin, P., Yang, X., Pan, M., & Ishitsuka, Y. (2021) · Nature Communications · doi:10.1038/s41467-021-27228-1 · Cited by 261
Self-citationApplications of Google Earth Engine in fluvial geomorphology for detecting river channel change
Boothroyd, R., Williams, R. D., Hoey, T., Barrett, B., & Prasojo, O. A. (2020) · Wiley Interdisciplinary Reviews Water · doi:10.1002/wat2.1496 · Cited by 171
Gardner, J., Yang, X., Topp, S., Ross, M. R. V., Altenau, E. H., & Pavelsky, T. M. (2020) · Geophysical Research Letters · doi:10.1029/2020gl088946 · Cited by 122
Self-citationEstimating River Channel Bathymetry in Large Scale Flood Inundation Models
Neal, J. C., Hawker, L., Savage, J. T. S., Durand, M., Bates, P., & Sampson, C. (2021) · Water Resources Research · doi:10.1029/2020wr028301 · Cited by 117
Global Observations of Riverbank Erosion and Accretion From Landsat Imagery
Langhorst, T., & Pavelsky, T. M. (2023) · Journal of Geophysical Research Earth Surface · doi:10.1029/2022jf006774 · Cited by 90
Self-citation
Metadata
- Authors
- Yang, X., Pavelsky, T. M., Allen, G. H., & Donchyts, G.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Texas A&M University (US)
- Deltares (NL)
- Venue
- IEEE Geoscience and Remote Sensing Letters
- Published
- 2020-02
- Volume
- 17
- Issue
- 2
- Pages
- 217-221
- ISSN
- 1545-598X (ISSN-L)
- 1558-0571
- OpenAlex
- W2955998290
- Cited by
- 156
- References
- 26
- Citations by year
- 2026: 31 · 2025: 26 · 2024: 21 · 2023: 18 · 2022: 16 · 2021: 24 · 2020: 19
- FWCI
- 10.62
- Type
- journal article
- Language
- en
- Open access
- green
- cdr.lib.unc.edu
- Licence
- Locations
- IEEE Geoscience and Remote Sensing Letters (publishedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- UNC Libraries
- Funders
- Jet Propulsion Laboratory
- Topics
- Physical Sciences › Earth and Planetary Sciences › Earth-Surface Processes › Hydrology and Sediment Transport Processes
- Physical Sciences › Environmental Science › Global and Planetary Change › Flood Risk Assessment and Management
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
- Keywords
- cloud detection, cloud shadows, Landsat imagery, Google Earth Engine, cloud computing, automated image processing, water resources management
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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.
A language model drew the claims from the paper on 2026-11-08, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
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