| 10 Feb 2026
A prototype algorithm for daily water hyacinth monitoring at Hartbeespoortdam, South Africa, from Sentinel-3 OLCI data
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.
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
Methods
Outcome/discussion
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.