the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 May 2026
Quantifying Mangrove Extent and Uncertainty: An Embedding-Driven Approach for Southeast Asia and Papua New Guinea
Abstract. Mangroves are an important ecosystem across Southeast Asia and Papua New Guinea, and accurate estimates of their occurrence and extent are critical. However, area estimates vary widely across studies, often because they rely on direct pixel counts without stratified sampling or design-based inference, making them sensitive to image and model biases that vary across space and time. This study evaluates whether satellite foundation-model embeddings can provide a more temporally consistent representation for mangrove monitoring while examining spatial transferability. We analyze the spatiotemporal structure of 64-dimensional AlphaEarth Foundations embeddings and apply a gradient-boosted tree classifier to generate annual mangrove probability maps for 2017–2024. These maps are combined with stratified probability sampling and design-based accuracy assessment to derive accuracies and unbiased area estimates with quantified uncertainty. The embeddings show clear mangrove–non-mangrove separability and relative temporal consistency in the embedding space, supporting generally stable regional classification performance (overall accuracy ≈0.80–0.82; AUC ≈0.92–0.93). While performance declines in heterogeneous coastal systems indicate remaining transferability limits, the results demonstrate that foundation-model embeddings provide a temporally consistent and operational basis for large-scale mangrove monitoring with robust, design-based area estimation. More broadly, embeddings offer a compact representation that supports feature-space analysis and more consistent interpretation of changes over time.
Status: open (until 03 Aug 2026)
- RC1: 'Comment on egusphere-2026-2205', Akkarapon Chaiyana, 29 Jun 2026 reply
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RC2: 'Comment on egusphere-2026-2205', Anonymous Referee #2, 20 Jul 2026
reply
This manuscript showcases an embedding-driven approach for mangrove extent mapping with uncertainty estimates. While this study clearly uses Alpha Earth embeddings for mangrove mapping, the manuscript requires several improvements to clarify the main message, presentation, and methodology.
General comment
The manuscript presents AE embeddings with 64-dimensional features combined with gradient boosting to generate annual mangrove maps. While the authors confirm that this approach achieves the greatest classification stability, they do not provide evidence that it improves conventional S1/S2-based methods or existing global maps such as WorldCover in terms of mangrove delineation. Before introducing a complex, large dataset (64 layers) as a go-to method for mapping, its efficiency relative to simpler approaches needs to be demonstrated.
The manuscript mentions integrating an embedding-based map with design-based area estimation (line 99). It also mentions correcting the map-based area estimation with design-based area estimation. It is not clear where the integration is performed or what it entails. The manuscript seems to suggest that the design-based area estimation corrects the map-based pixel counting and thereby integrates the two. In reality, these two are different approaches to estimating areas. The map-based approach is subject to mapping error and therefore biased. The design-based (sample-based) approach is unbiased as it uses statistical probability sampling. The Olofsson formulas for area estimation, in fact, only use the reference (sampled data) equation 6. Hence, this is a sample-based area estimation under design-based inference. So there is no integration of design-based area estimation and AE-embedding-based maps, other than that the generated maps are used for stratification to draw the reference sample. If one wants to integrate two estimates (map-based and sample-based), model-assisted area estimation is a way to increase the precision of the area estimation. These differences should be clearly presented in this manuscript and the “integration” needs to be clarified.
Spatial similarity is assessed using a distance-based similarity metric and Montecarlo. It is not clear why more common moran’s I and kriging variances are not used. In particular, the latter can clearly show the spatial dependency of the variable with clear spatial structure and range. In addition, the motivation of spatial similarity assessment and its link with the AE variables are not clear. Spatial similarity is highly dependent on underlying landscape homogeneity and will vary naturally from place to place. What value does this bring besides an area being spatially homogeneous or heterogeneous? In particular, this assessment is not compared against Sentinel 2 or other satellite-based similarity assessments; therefore, the value of this analysis or the advantage of using AE is not clear. Also, how is the temporal variability calculated? Please clarify.
The sample size calculation and allocation need further explanation; what was the sample size per region? What was the expected accuracy or standard error used for estimating and allocating sample size? Equation 7, what is ph? What value did you use? A multi-year sample was collected; are they different (in terms of spatial location) for each year, or are they collocated? In other words, only once sample locations were selected, and the interpretation done for each year? Please clarify. If for the latter, change in mangrove area can be estimated, it would be informative to see the change area with uncertainty estimates.
Specific comments
52-55: please provide evidence or reference for this statement.
Figure 2: Are the forest samples the training sample or a stratified random sample used for accuracy and area estimation?
Table 2: Please show this table as a figure with simple error ranges on a bar; this way, the temporal fluctuations can be clearly seen. In fact this can be directly compared with the map based pixel counting.
Table 3: Please provide the confidence ranges for the accuracy estimates. There is a systematic tendency of high omission errors in the classification. This needs to be clearly communicated and highlighted in the manuscript.
Samples: multiple times, the word “samples” was used when referring to the sample size or sample locations. A sample means a set of sample units (see the Tyukavina et al 2025 glossary and terminology explanation). I recommend using the correct term sample units or sample sites rather than samples, as I believe this study does not use multiple sets of samples.
Citation: https://doi.org/10.5194/egusphere-2026-2205-RC2
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This study proposes a framework for quantifying mangrove extent across Southeast Asia and Papua New Guinea using a Gradient Boosting Tree (GBT) model trained on the AlphaEarth Embedding dataset. The manuscript is generally well organized, and the overall study design is presented according to scientific standards. However, the manuscript requires substantial improvement in clearly articulating its key scientific contributions and novelty. At present, the manuscript reads more like a project description than a research study, with insufficient emphasis on the methodological innovations, scientific significance, and broader implications of the proposed approach. The authors should better clarify how their work advances the current state of the art and distinguish it from existing mangrove mapping studies. Therefore, I recommend major revision before the manuscript can be considered for publication.