A prototype algorithm for daily water hyacinth monitoring at Hartbeespoortdam, South Africa, from Sentinel-3 OLCI data

Penning de Vries, Marloes; Shoko, Cletah; Dube, Timothy; Thamaga, Kgabo Humphrey; Salama, Suhyb

doi:10.5194/egusphere-2026-403

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https://doi.org/10.5194/egusphere-2026-403

Preprints

10 Feb 2026

| 10 Feb 2026

A prototype algorithm for daily water hyacinth monitoring at Hartbeespoortdam, South Africa, from Sentinel-3 OLCI data

Marloes Penning de Vries, Cletah Shoko, Timothy Dube, Kgabo Humphrey Thamaga, and Suhyb Salama

Abstract. Water HYacinth (WHY) is one of the world’s most disturbing invasive aquatic plant species, characterised by a high spatial and temporal variability. Remote sensing is a valuable approach to monitor WHY, as the dense, floating mats of vegetation can be detected using various satellite instruments, for example, OLCI on Sentinel-3. The multi-spectral instrument features only moderate spatial resolution (300 m), however, it achieves global coverage in two days, and with two instruments currently in orbit, it provides an opportunity to monitor WHY at near-daily resolution. This is crucial, considering that WHY cover patterns are highly variable due to the plants’ rapid reproduction and the influences of wind and hydrodynamics. We present the development of an algorithm for the creation of daily WHY maps by: (1) deriving WHY cover patterns from both OLCI instruments using the Normalized Difference Vegetation Index, NDVI; (2) merging the data sets into one with near-daily resolution; and (3) filling the gaps (due to missing observations or cloud interference) using a spatial-temporal interpolation scheme. We show that the gap-filling strategy leads to a consistent daily time series of WHY cover for the study region and increases the number of days with observations by 55%. A leave-one-out analysis showed that the interpolation algorithm performs well even for longer periods without observations, unless WHY cover patterns change abruptly. The presented algorithm is computationally light-weight and, although developed for Hartbeespoortdam Reservoir (South Africa), is easily adaptable for use with other water bodies. The prototype WHYmapping algorithm was used to analyse eighteen months of data (July 2022 – December 2023). In the future, long time series of daily WHY maps may provide an evaluation tool for WHY management and benefit water management strategies directly by allowing continuous monitoring of WHY.

Received: 23 Jan 2026 – Discussion started: 10 Feb 2026

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Marloes Penning de Vries, Cletah Shoko, Timothy Dube, Kgabo Humphrey Thamaga, and Suhyb Salama

Status: final response (author comments only)

RC1:
'Comment on egusphere-2026-403', Anonymous Referee #1, 03 Jul 2026
General comments
The authors present a prototype algorithm that exploits time series of spectral indices derived from Sentinel-3 OLCI data to map floating aquatic vegetation cover. The approach incorporates multi-sensor data merging and spatio-temporal gap-filling capabilities. As a demonstration, the algorithm is applied to an 18-month dataset covering Hartbeespoortdam, a small artificial reservoir in South Africa that is heavily affected by the proliferation of water hyacinth, an invasive floating macrophyte.
The proposed framework has potential advantages over existing approaches, particularly regarding the gap-filling strategy, which appears straightforward and effective, although further evidence would be required to fully demonstrate its robustness and performance. Nevertheless, the manuscript presents several critical shortcomings that are especially relevant for a study introducing a new Earth Observation (EO)-based methodology.
First, there appears to be a mismatch between the stated target of the algorithm and the physical basis of the input variables. While water hyacinth is presented as the target species, the spectral features used as inputs are not specific to water hyacinth and are unable to discriminate among different types of floating materials characterized by pronounced red-edge signatures, such as floating macrophytes, algal scums, or similar vegetation aggregates. Second, the demonstration of the algorithm is limited both spatially and temporally. As a result, claims regarding transferability and broader applicability remain largely speculative and are not supported by empirical evidence. Third, several methodological aspects would benefit from further refinement and a more in-depth assessment, particularly concerning use of TOA radiance as input and potential anisotropy distortions.
In light of these considerations, I suggest that the authors further refine and optimize the algorithm for the specific conditions of Hartbeespoortdam and reposition the study as a comprehensive case study of water hyacinth—or, more broadly, floating vegetation—monitoring in this system. Such an approach could be significantly strengthened through the integration of historical EO datasets, for example by combining MERIS, OLCI, and potentially MODIS observations, thereby extending the analysis over a much longer temporal period and providing a more robust assessment of long-term vegetation dynamics.

Specific comments
Intro and rationale
The use of an acronym for the target species name is unnecessary and, in my opinion, does not improve readability. Referring to the species by its common name, or alternatively using generic expressions such as "the target species" where appropriate, would be sufficient to avoid excessive repetition.

The rationale presented in the Introduction appears to underestimate the extent of the existing literature on water hyacinth ecology, management, and control. Research on the biology of the species and on management approaches has been substantial, particularly over the last 10–15 years. A quick Scopus search using the query “TITLE-ABS-KEY ("water hyacinth" AND crassipes AND management approach*)” returns more than one hundred publications, including more than 30 review papers. This seems to contradict the statement suggesting a lack of systematic assessments of management strategies. Indeed, the subsequent paragraphs of the Introduction already cite a few relevant studies, indicating that a considerable body of literature is available and should be discussed more comprehensively.

The manuscript describes the proposed algorithm as computationally lightweight multiple times; however, this claim is not substantiated by quantitative evidence. No computational benchmarks or comparisons with alternative approaches are provided. Ideally, the authors should provide objective performance metrics demonstrating the computational efficiency of the method to support such claims.

Methods
OLCI is a wide-swath instrument characterized by substantial variability in viewing zenith angle (VZA) across the scene. For targets with pronounced anisotropic reflectance properties, such as aquatic environments and floating vegetation, VZA effects cannot be assumed negligible. The manuscript does not appear to account for directional effects, but the potential impact on the temporal consistency of the spectral indices used is relevant to understanding and interpreting (apparent) temporal changes in vegetation cover.

The proposed approach relies primarily on NDVI (based on red and near-infrared reflectance). However simple and seen as generally effective, this spectral information is insufficient to distinguish among different types of photosynthetically active floating materials. Similar NDVI values may be exhibited by different floating or floating-leaved macrophyte species, as well as by dense accumulations of phytoplankton scums. The latter are especially common in eutrophic, cyanobacteria-dominated systems such as Hartbeespoortdam. Consequently, the method appears more suitable for detecting generic floating vegetation (or floating phytomass) rather than specifically targeting water hyacinth.

The only discussion of direct field observations (L379–383) effectively reinforces the concerns raised above. Rather than supporting the specificity of the proposed approach for water hyacinth detection, these observations highlight the presence of multiple floating materials that may contribute to the observed signal. As a result, the field evidence appears to undermine, rather than strengthen, the central assumption that the algorithm is detecting water hyacinth specifically.

The use of TOA radiometric quantities as input data represents a major limitation. TOA observations do not provide standardized surface reflectance measurements and are therefore intrinsically affected by changes in atmospheric conditions, illumination and viewing geometry. This severely limits the transferability of the methodology across seasons, years, sensors, and sites.

Related to the previous point, the manuscript attributes part of the observed discrepancies between Sentinel-3A and Sentinel-3B to calibration differences between the two OLCI instruments. This explanation does not appear convincing. Instrument calibration differences are unlikely to account for NDVI discrepancies on the order of 0.1. Variations in viewing geometry (particularly VZA), atmospheric conditions, and the use of TOA rather than atmospherically corrected reflectance are more plausible explanations and should be investigated before drawing conclusions regarding sensor-related biases.

More generally, the manuscript does not adequately disentangle the respective contributions of instrument characteristics, observation geometry, atmospheric variability, and actual changes in floating vegetation cover. Without such analysis, there is a risk that part of the outcomes of the algorithm may reflect biases in input data and quantities more than water hyacinth variability.

Outcome/discussion
The assessment of the gap-filling procedure is very limited in scope, only five days, and does not incorporate actual data to be used as reference for assessing errors. While the results are qualitatively encouraging, the evidence provided is insufficient to support broader conclusions regarding the reliability, or applicability of the proposed approach.

The demonstration use case (section 3.3) is similarly limited in temporal extent. It is not clear why the analysis covers only approximately 1.5 years of observations. Given the authors' repeated emphasis on the computational efficiency and lightweight nature of the algorithm, a longer time series analysis would appear both feasible and highly beneficial for demonstrating robustness, transferability, and practical value.

In several instances, the Discussion appears to overstate the actual findings of the study, such as the following:

L335–338: Based on the spectral information used, the prototype is capable of detecting floating photosynthetically active material more generally, including floating aquatic vegetation and potentially algal scums, not only water hyacinth. As this, the statement is not fully supported by the actual results presented.

L344–346: The text refers to agreement with observations, yet no actual observations are presented in this section. What is shown is a comparison between map products. Without corresponding field observations or independent validation data, the statement should be rephrased more cautiously.

L365–366: While I understand the authors' concern that cloud-screening procedures applied to Level-2 products may be overly conservative for their intended application, the advantages of using bottom-of-atmosphere (BOA) reflectance generally outweigh the disadvantages associated with TOA spectral quantities. I therefore encourage the authors to perform an exploratory comparison using Sentinel-3 Level-2 products to evaluate the sensitivity of the approach to standard atmospheric correction.

L367–369: The discussion of spectral ratios is not accurate: band ratios – and NDVI is not strictly a simple ratio - do not eliminate the effects of viewing geometry or atmospheric variability. At best, they may partially attenuate such effects under certain conditions. Consequently, VZA- and atmosphere-induced variability cannot be assumed negligible solely because an index-based approach is employed.

L371–374: I do not see a convincing reason why the properties attributed here to TOA radiance/reflectance would not also be met, and generally met more consistently, by BOA reflectance products. Since BOA retrievals aim to remove atmospheric effects and provide physically comparable surface reflectance measurements, the argument presented in favour of TOA data requires further justification.

More generally, a recurring issue throughout the manuscript is that the limitations of the input data and spectral approach are frequently mentioned and, in some cases, explicitly acknowledged, yet their implications are subsequently downplayed when interpreting the results. Given the lack of species-specific discriminatory capability, the use of TOA radiometry, the potential influence of viewing geometry effects, and the limited validation presented, the evidence supporting the performance of the proposed algorithm remains relatively weak.

Technical issues
In addition, I list here some punctual issues of various relevance that, in my opinion, need the authors' attention. These are referenced by manuscript line:
L31: The scientific name of water hyacinth has been changed to Pontederia crassipes few years ago. Better use this accepted name.
L73-74: is this based on microwaves (SAR) fitting the rationale of this work?
L121: what does it mean that NDVI can be biased by very still waters? I cannot understand it.
L141-142: do you mean that L1C data are not gridded originally?
Table 2: class taxonomy is not consistent here: class 1 relates to a density attribute, while class 2 on a confidence attribute. Either use consistent dichotomies (sparse/dense, uncertain/confident or something similar).
Fig. 3: colour scale for panels E and F should be discrete, not continuous.
Fig. 7-8: To provide a clearer and more synthetic overview, they should be merge into a unique figure.
L394-397: The concluding remarks would also benefit from a more balanced perspective. I would suggest avoiding conclusions that may be perceived as overly Eurocentric or technology-centric, particularly when discussing environmental management challenges that are highly dependent on local conditions, resources, and governance frameworks.
Citation: https://doi.org/10.5194/egusphere-2026-403-RC1
- RC2: 'Reply on RC1', Anonymous Referee #2, 22 Jul 2026
  
  1. The manuscript lacks validation against independent ground observations or
  
  high-resolution UAV/Sentinel-2 derived reference maps. Although the leave-
  
  one-out analysis evaluates interpolation performance, it does not quantify the
  
  accuracy of the water hyacinth detection itself. Please include quantitative
  
  validation metrics (e.g., Overall Accuracy, F1-score, IoU, or Kappa
  
  coefficient) using independent reference data.
  
  2. The NDVI thresholds (NV_low and NV_high) were derived from a single
  
  reference date (1 September 2022). It is unclear whether these thresholds
  
  remain valid under varying seasonal, atmospheric, and illumination
  
  conditions. A sensitivity analysis or multi-season threshold calibration would
  
  improve the robustness of the proposed algorithm.
  3. The methodology relies on Level-1C Top-of-Atmosphere (TOA) radiances
  
  instead of atmospherically corrected surface reflectance. The authors should
  
  justify why atmospheric correction was omitted and discuss the possible
  
  influence of aerosols, haze, and changing atmospheric conditions on NDVI-
  
  derived classifications.
  4. The study would be strengthened by comparing the proposed NDVI-based
  
  approach with alternative vegetation indices (e.g., FAI, NDAVI, WAVI) or
  
  machine learning classifiers reported in previous literature. Such comparisons
  
  would better demonstrate the advantages and limitations of the proposed
  
  algorithm.
  5. Although the gap-filling algorithm successfully increases temporal coverage,
  
  interpolation may smooth rapid spatial changes caused by wind-driven
  
  movement of floating vegetation. The manuscript should quantify the
  
  uncertainty introduced by interpolation and discuss its implications for
  
  operational monitoring during abrupt water hyacinth redistribution events.
  6. The authors state that the algorithm can easily be adapted to other water
  
  bodies; however, no demonstration or discussion is provided for reservoirs
  
  with different morphologies, trophic conditions, or vegetation densities. A
  
  discussion of algorithm transferability and required parameter adjustments
  
  would enhance the paper.
  
  7. Since the algorithm is described as computationally lightweight, the
  
  manuscript should report processing time, computational requirements,
  
  software environment, and scalability for larger study areas. These details are
  
  important for assessing operational implementation.
  8. The moderate spatial resolution (300 m) of Sentinel-3 OLCI may fail to detect
  
  small or fragmented water hyacinth patches and may introduce mixed-pixel
  
  effects near shorelines. The manuscript should include a more detailed
  
  discussion of these limitations and their influence on monitoring accuracy,
  
  particularly for smaller reservoirs.
  9. The authors should justify why Sentinel-3 OLCI was selected instead of
  
  MODIS (MOD09GQ/MYD09GQ) products. MODIS provides near-daily
  
  observations and 250 m NDVI products, which offer slightly finer spatial
  
  resolution than the 300 m OLCI data and have been widely used for vegetation
  
  monitoring. A discussion comparing the advantages and limitations of OLCI
  
  and MODIS (e.g., spatial resolution, revisit frequency, spectral characteristics,
  
  radiometric performance, cloud sensitivity, and suitability for floating water
  
  hyacinth detection) would strengthen the manuscript and better justify the
  
  choice of sensor
  
  Citation: https://doi.org/10.5194/egusphere-2026-403-RC2

Marloes Penning de Vries, Cletah Shoko, Timothy Dube, Kgabo Humphrey Thamaga, and Suhyb Salama

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Short summary

Water hyacinth (WHY) are one of the most costly aquatic invasive species. Regular monitoring of WHY would the management of infested waterboddies. We present an algorithm to derive daily maps of WHY from satellites, featuring an interpolation scheme, which led to 55% more daily WHY maps of Hartbeespoortdam Reservoir. The algorithm is light-weight and can be run on a cloud-computing platform. This allows applications in (sub-)tropical regions with limited access to computing power and/or funding.


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