Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies > 4.3. Strong vs. weak supervision: Challenges, opportunities
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Most of the glacial lake studies that apply ML/DL methodologies (Fig. 3) rely on supervised learning, which depends heavily on labeled datasets. However, generating ground truth annotations is timeconsuming and resource-intensive. To mitigate this, it is essential to develop more efficient methods for generating reference labels. Another approach is to adopt weakly supervised learning (Chapelle et al., 2009; Karamanolakis et al., 2021), which uses minimal, noisy, or incomplete labels. This will reduce annotation effort, enabling broader and more scalable application of ML and DL analyses. Additionally, it will help fully leverage the vast and ever-growing volumes of available remote sensing data. An example is Ortiz et al. (2022), who applied varying degrees of weak supervision. They employed historically guided U-Net, morphological snakes, and DEep Level Set Evolution (DELSE), based on the assumption that glacial lakes evolve gradually over time. They used low-resolution historical glacial lake labels to guide the segmentation of more recent high-resolution satellite imagery and Bing maps. Similarly, Zhao et al. (2023) introduced weak supervision using NDWI within a contrastive loss-based Siamese neural network. They learned glacial lake representations by maximizing the similarity between input satellite images and their augmentations, requiring no ground truth and only minimal supervision. However, the full potential of weakly supervised learning remains systematically underexplored in glacial lake studies. This presents a significant research opportunity.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies > 4.4. Class categories, imbalance
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Most supervised approaches formulate binary classification tasks to distinguish glacial lake pixels from the background. A few exceptions (Veh et al., 2018; Dirscherl et al., 2020, 2021; Halberstadt et al., 2020; Wendleder et al., 2021) incorporated additional classes like snow/ice, shadow, rock/land/sediment, debris, slush, firn, and cloud. Halberstadt et al. (2020) included even fine-grained classes such as blue ice, flowing ice, shallow lakes, deep lakes, cloud shadow, sunlit rock, and shadowed rock. In rare cases, the number of classes varied based on input data. For instance, Dirscherl et al. (2021) applied binary classification for S1 data but four-class classification for S2 data.

Both binary and multi-class classification can achieve accurate lake detection. Binary approaches are often simpler. Multi-class methods offer the added benefit of detailed surface characterization, at the cost of higher-quality training data and more complex models.

data/('Monitoring earths glacial lakes from space with machine learning', '.pdf')_extraction.md


Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies > 4.4. Class categories, imbalance
Type: figure
Figure

Image /page/7/Figure/2 description: An image displaying two donut charts side-by-side. The chart on the left is fully visible and shows the distribution of different categories. The largest segment is 'Cross entropy (12)' in blue-gray, followed by 'Dice (7)' in light green. The other segments are 'Others (4)' in yellow, 'Focal (3)' in teal, 'Tversky (1)' in orange, and 'Lovasz hinge (1)' in pink. The chart on the right is partially visible, showing segments for 'Adabelief' (number cut off) in green, 'SGD (7)' in a larger green segment, and 'AdaMax (1)' in blue. There is also a red segment with no visible label.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies > 4.4. Class categories, imbalance
Type: text

- (a) Loss functions (based on 24 publications)
- (b) Optimization strategies (23 publications)

Fig. 5. Distribution of hyperparameters in deep learning-based glacial lake studies (number of publications in brackets). Some studies reported multiple loss functions (Basit et al., 2022; Cao et al., 2024) or optimizers (Chatterjee et al., 2022). More details are in (Appendix F).

When framed as a two-class problem, the "lake" class is typically underrepresented, leading to class imbalance (He and Garcia, 2009). This uneven class distribution can bias the model toward the majority class: "background", resulting in poor performance on the minority class

Standard strategies to address class imbalance include tailored loss functions (Section 4.5.1), strict evaluation metrics (Section 4.6), class-dependent data augmentation (Section 4.5.3), and undersampling the majority class (Dirscherl et al., 2020; Yuan et al., 2020). Regardless of the strategy employed, addressing class imbalance is crucial in glacial lake studies and is strongly encouraged.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies > 4.5. Deep learning approaches > 4.5.1. Loss functions and optimization schemes
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A loss function quantifies the difference between a model's predictions and the actual ground truth labels, guiding the optimization process during model training (Wang et al., 2022b). By minimizing the loss, the model iteratively adjusts its parameters to improve predictions. In glacial lake studies, cross-entropy (He et al., 2021; Kaushik et al., 2022, etc.) and Dice loss (Li et al., 2020; Hu et al., 2024, etc.) functions are the most commonly used (Fig. 5).

Cross-entropy is well-suited for multi-class classification, providing a straightforward approach to measure the divergence between predicted probabilities and true labels (Zhang and Sabuncu, 2018). It is also effective for binary classification. To address class imbalance, several glacial lake studies (Ortiz et al., 2022; Hardie et al., 2024, etc.) successfully employed weighted variants of cross-entropy. These variants assign higher weights to minority "lake" class samples during model training.

For binary semantic segmentation tasks, dice loss is particularly effective. It directly optimizes the overlap between predicted and actual foreground regions, mitigating the impact of class imbalance. While it can be extended to multi-class segmentation by computing perclass dice scores, its ability to balance foreground and background contributions makes it especially valuable in tasks like glacial lake detection (Sudre et al., 2017). Tversky loss (Wu et al., 2020), an extension of dice loss, further enhances flexibility. It adjusts penalties for false positives and false negatives, making it useful when prioritizing precision or recall (Salehi et al., 2017).

Optimization strategies complement loss functions. Adaptive Moment (AdaM) is widely used (Fig. 5), followed by Stochastic Gradient Descent (SGD). AdaM combines the benefits of adaptive learning rates and momentum. This makes it particularly effective in handling sparse gradients or non-stationary objectives (Kingma and Ba, 2015).

Advanced variants like AdaMax and AdaBelief have been explored by Dirscherl et al. (2021) and Chatterjee et al. (2022), respectively. AdaMax extends AdaM to work effectively with infinite norms. It offers greater stability in sparse gradient scenarios and non-convex optimization (Kingma and Ba, 2015). AdaBelief modifies the second-moment estimation, leading to faster convergence and better general-ization (Zhuang et al., 2020).

Adam (15)

In contrast, SGD is used due to its relative simplicity and effectiveness in large-scale problems. It is especially useful when computational efficiency is critical (Bottou, 2010). However, in its basic form, SGD may struggle with convergence on complex loss surfaces. Enhancements like momentum or learning rate scheduling, as demonstrated by some glacial lake studies (Yuan et al. (2020), Wang et al. (2021) and Hu et al. (2024)), can address these issues.

In glacial lake studies, depending on the number of classes, it is recommended to use any of the above loss functions that effectively tackle class imbalance. SGD optimization with momentum is recommended for large datasets or when resources are limited. AdaM variants are encouraged for complex, sparse data requiring quick model convergence.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies > 4.5. Deep learning approaches > 4.5.2. Pre-training, transfer learning, & model adaptations
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Instead of training from scratch, fine-tuning a DL model pre-trained on a large, labeled dataset mitigates issues like slow convergence and overfitting. Transfer learning allows adapting pre-trained model weights, reducing the need for extensive labeled data while enhancing generalization.

In satellite remote sensing, transfer learning is facilitated by publicly available large-scale datasets. Examples are *BigEarthNet* (Sumbul et al., 2021), *EuroSAT* (Helber et al., 2019), *SpaceNet* (Shermeyer et al., 2020), and *ImageNet* (Deng et al., 2009; Krizhevsky et al., 2017). These datasets allow models to learn fundamental spatial features – such as edges, textures, and object structures – which can be repurposed for tasks like glacial lake segmentation (Weiss et al., 2016).

For instance, Qayyum et al. (2020) employed transfer learning for glacial lake mapping. They used *EfficientNet* (Tan and Le, 2019) as the backbone for a U-Net model, fine-tuning its higher layers for glacial lake detection.

Transfer learning is effective even when the original and target tasks differ significantly. While this capability has been extensively explored in related fields, such as lake ice monitoring, transfer learning remains underutilized in glacial lake studies. For instance, Tom et al. (2022) demonstrated that a DeepLabv3+ model pre-trained on closerange imagery for a computer vision task could be successfully adapted for lake ice monitoring using SAR satellite data. This exemplifies the versatility of transfer learning and presents a promising avenue for glacial lake remote sensing research.

One challenge is that most DL models used for glacial lake studies were originally designed for generic computer vision tasks. Such models typically support only 3–4 input channels (e.g., RGB, RGB-depth). This design choice simplifies transfer learning but restricts the ability to fully exploit multispectral, hyperspectral, and radar-based remote sensing data. Only a few studies used more than four channels (Wu et al., 2020; He et al., 2021; Hu et al., 2024; Tang et al., 2024). Others have worked

around this limitation by empirically selecting the most relevant input spectral features (Section 3.3) or using automated feature importance analysis. However, such approaches may not have exploited the full potential of multi-source remote sensing data.

Expanding model architectures to accommodate a greater number of input channels, such as a dozen or more spectral bands, is technically straightforward. However, it presents a trade-off. While such modifications enhance the model's ability to process diverse remote sensing data, they prevent the direct reuse of pre-trained weights, necessitating training from scratch. This increases computational costs and the risk of overfitting.

Furthermore, standard DL architectures are not optimized for radarspecific features such as phase, backscatter, and polarization. This underscores the need for task-specific model adaptations. Therefore, to maximize the benefits of transfer learning for large-scale, data-efficient glacial lake studies, it is necessary to redesign existing architectures. These should accommodate a wider range of remote sensing inputs while still leveraging pre-trained feature representations.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies > 4.5. Deep learning approaches > 4.5.3. Importance of data augmentation
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Augmentation in DL is a strategy to mitigate data scarcity. By transforming existing data, this technique expands and diversifies the training dataset. It improves model robustness, generalization, and efficiency through exposure to diverse data variations, all without requiring additional data collection or labeling. However, the effectiveness of augmentation strategies remains largely empirical and highly dependent on the dataset and model.

Glacial lake studies have applied spatial transformation operations such as patch flipping (Wang et al., 2022c, etc.), patch rotation (Cao et al., 2024, etc.), and patch mirroring (He et al., 2021, etc.). Spectral distribution modifications were also explored. Wu et al. (2020) adjusted image saturation and brightness. Zhao et al. (2023) employed techniques like color jittering, random erasing, blurring, noise addition, and grayscale adjustments.

In Dirscherl et al. (2021), augmentation strategies were tailored per patch depending on the number of lake pixels in each patch. To address class imbalance, they oversampled the image patches with the underrepresented 'lake' class. Additionally, they augmented challenging non-water patches (wet snow, shadow pixels, etc.).

Notably, only a few papers quantitatively evaluated the performance impact of individual operations. For instance, Zhao et al. (2023) found that spatial transformations ( $F_1$  score: 0.66) slightly outperformed spectral modifications ( $F_1$  score: 0.65). Among individual techniques, image flipping was most effective ( $F_1$  score: 0.68), followed by color jittering ( $F_1$  score: 0.67) and image rotation ( $F_1$  score: 0.66). Based on these findings, they adjusted the probabilities of different augmentations to prioritize the most impactful ones.

Augmentation is a common practice in glacial lake studies. However, due to the limited number of studies that evaluate the impact of specific techniques quantitatively, it is difficult to generalize their utility across models and datasets. Future studies should systematically evaluate and optimize augmentation strategies.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies > 4.5. Deep learning approaches > 4.5.4. Recommended strategies
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Table 2 outlines recommended DL strategies for glacial lake detection under different scenarios. While outcomes may vary with model architecture and dataset characteristics (e.g., sensor type and resolution, region, class distribution, reference label noise), these strategies serve as empirically grounded starting points for method selection and adaptation.

Combining weak supervision (Section 4.3) with transfer learning (Section 4.5.2) offers a promising research direction. Ultimately, model selection should be driven by data availability. Supervised methods like U-Net are well-suited for data-rich environments if no transfer learning is involved. However, in data-scarce regions, weakly supervised approaches and pre-trained DL architectures offer viable alternatives.

Though DL has emerged as an exceptionally powerful tool in glacial lake remote sensing, there are some constraints. Firstly, DL models are relatively data-hungry, requiring large amounts of annotated reference data for training. Nevertheless, given the statistical nature of DL methodologies, performance improves with a broader and more diverse training dataset. However, fine-tuning may be necessary when applying such models to new regions or time periods. Secondly, DL methods require substantial computational resources, typically demanding powerful Graphics Processing Unit(s).


Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies > 4.6. Choosing right evaluation metrics
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Standard metrics such as recall (producer's accuracy),  $F_1$  score, precision (user's accuracy), IoU, and overall classification accuracy are the most commonly used in learning-based glacial lake studies. These metrics are depicted by the larger circles in Fig. 6. Recall measures the proportion of actual glacial lakes correctly identified, regardless of false positives. Conversely, precision evaluates the proportion of detected lakes that are actual lakes, reducing false identifications. IoU measures the overlap between predicted and actual truth, providing a robust metric for image segmentation tasks. The  $F_1$  score balances precision and recall. Overall accuracy reflects the proportion of correctly classified pixels (both lake and background) in relation to the total number of pixels in the dataset.

Some studies employ specialized metrics. For instance, the Tversky index (Wu et al., 2020) is designed to handle class imbalance while the Fréchet distance (Ortiz et al., 2022) measures shape similarity. The coefficient of determination (Kaushik et al., 2022) evaluates the agreement between the predicted and reference lake boundaries. These tailored metrics highlight a growing interest toward addressing nuanced aspects of performance evaluation.

Overall classification accuracy was relatively more commonly reported in early ML/DL studies (Fig. 6). However, overall accuracy alone is inadequate for class-imbalanced datasets, as it can misleadingly inflate performance results. Metrics such as IoU,  $F_1$  score, precision, recall, and the Tversky index, which are robust to class imbalance, are strongly recommended. While overall accuracy can provide useful context when combined with these stricter metrics, it should not be reported in isolation.

Additionally, some studies (He et al., 2021; Chen et al., 2022, etc.) reported the Kappa coefficient (Fig. 6). However, Pontius and Millones (2011) highlighted that Kappa indices can be misleading or flawed, particularly in remote sensing applications. These indices compare accuracy against a baseline of randomness, which is an unrealistic reference for map construction. This makes Kappa coefficient difficult to interpret and, in some cases, undefined. Consequently, its use is not recommended. The recent trend of reduced Kappa coefficient usage relative to other metrics (Fig. 6) aligns with this recommendation.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies > 4.7. Performance inter-comparison: Insights, gaps, challenges
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When proposing a novel glacial lake product, it is essential to benchmark its performance both quantitatively and qualitatively. This comparison should be made against existing state-of-the-art methods to substantiate performance gains. However, existing comparisons often differ in datasets, learning strategies, and evaluation metrics. This makes it difficult to draw definitive conclusions.

Qayyum et al. (2020) compared their U-Net model with *GLakeMap* (Wangchuk and Bolch, 2020), an RF-assisted rule-based thresholding approach. Both methods performed well on large lakes. However, U-Net outperformed *GLakeMap* on small lakes. This is likely due to the higher spatial resolution of PlanetScope (3 m) compared to S2 (10 m) used by the latter.

In contrast, using S2 data, Basit et al. (2022) outperformed (IoU 0.8 vs. 0.71) Siddique et al. (2023), who relied on PlanetScope imagery. Interestingly, both used a U-Net model with an *ImageNet*-pretrained


Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies > Table 2
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DL recommendations for glacial lake detection under common application scenarios. CE stands for Cross Entropy.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies > Table 2 > Extreme class imbalance
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- · Use loss functions such as Focal, Tversky, Dice, weighted CE (avoid unweighted CE).
- · Augment the training set to increase underrepresented class samples.
- Optimizers do not directly address class imbalance. However, Adam and SGD with momentum can support stable and faster convergence when used with imbalance-aware loss functions. Examples: Focal/Tversky + Adam, Dice + SGD (with momentum).


Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies > Table 2 > Data scarcity
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- · Use transfer learning and data augmentation.
- · When training from scratch, prefer relatively lightweight models (e.g., U-Net with MobileNetV2 or EfficientNet-BO encoders) over deeper architectures like DeepLabv3+.
- · Optimizers like Adam can accelerate convergence on small datasets, but may require strong regularization (e.g., dropout) to prevent overfitting.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies > Table 2 > imited computational resources:
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- Use models with lightweight backbones (e.g., U-Net or DeepLabv3+ with MobileNetV2 or EfficientNet-B0).
- Use computationally efficient loss functions such as weighted CE together with memory-efficient optimizers like SGD (with momentum). While Adam may converge faster, it has relatively higher memory overhead and should be used selectively.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies > Table 2 > Small lake detection:
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- · Use attention-based models (e.g., attention U-Net by He et al., 2021), noting added computational cost.
- Use DeepLabv3+ with Atrous Spatial Pyramid Pooling (ASPP). Skip connections (in U-Net) and multi-scale context (in DeepLabv3+) enhance small target detection.

EfficientNetB0 backbone. Several factors likely contributed to these performance differences. While spatial and spectral resolution both influence model performance, their impact also depends on the training strategy. EfficientNetB0, optimized for larger datasets, may have underfitted on PlanetScope's limited spectral bands, hindering its ability to learn discriminative features. While PlanetScope offers finer spatial resolution, it lacks broader spectral coverage, particularly in the ShortWave InfraRed (SWIR) range. This limitation could reduce lakebackground separability. The datasets also differed in size (1200 images for Basit et al. (2022) vs. 3525 for Siddique et al. (2023)). The loss function used also varied, with Basit et al. (2022) employing both focal and Jaccard loss, while Siddique et al. (2023) used only focal loss. The use of Jaccard loss, which directly optimizes for IoU, likely contributed to Basit et al. (2022)'s higher performance.

Some studies have compared DL models with ML counterparts and thresholded spectral indices. For instance, Qayyum et al. (2020) compared their *Efficient-U-Net* (CNN) model against standard ML models. They reported an  $F_1$  score of 0.94, significantly outperforming SVM (0.78) and RF (0.75). This highlights the advantages of U-Net.

Similarly, Wu et al. (2020) compared their U-Net model with RF and thresholded Modified Normalized Difference Water Index (MNDWI). They found that both U-Net and RF outperformed MNDWI in cases involving mountain shadows and frozen lakes. Their U-Net model effectively learned the spatial relationships between neighboring pixels. U-Net exhibited fewer misclassifications in low-reflectivity areas compared to RF.

Some DL-based studies have reported modest results as well. For example, Yuan et al. (2020) evaluated CNNs against RF and SVM, however observed only marginal improvements. This was because the number of test samples was not sufficient. Interestingly, all three approaches (CNN, SVM, RF) achieved more than 98.5% overall accuracy, recall and precision. This indicates a weak test set rather than an inherent shortcoming of CNN, thereby limiting the ability to discern meaningful differences in performance.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies > Table 2 > Small lake detection:
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the spatial relationships between neighboring pixels . U - Net exhibited fewer misclassifications in low - reflectivity areas compared to RF . Some DL - based studies have reported modest results as well . For example , Yuan et al . ( 2020 ) evaluated CNNs against RF and SVM , however observed only marginal improvements . This was because the number of test samples was not sufficient . Interestingly , all three approaches ( CNN , SVM , RF ) achieved more than 98 . 5 % overall accuracy , recall and precision . This indicates a weak test set rather than an inherent shortcoming of CNN , thereby limiting the ability to discern meaningful differences in performance .

Few studies have systematically evaluated different deep learning architectures and backbone choices. For instance, He et al. (2021) incorporated a self-attention mechanism (Vaswani et al., 2017; Ghaffarian et al., 2021) into U-Net. This resulted in only a slight improvement ( $F_1$  score: 0.69) compared to the baseline U-Net ( $F_1$  score: 0.68). Similarly, Hu et al. (2024) enhanced U-Net by integrating a residual attention mechanism. This led to a 1.5% increase in  $F_1$  score and a three-fold acceleration in convergence. Wang et al. (2021) introduced ACFNet, comparing it against Wu et al. (2020)'s U-Net and achieving a higher  $F_1$  score (0.91 vs. 0.88). While incremental, these improvements highlight the potential impact of architectural enhancements in DL models

Cao et al. (2024) conducted a comprehensive comparison of SVM, RF, U-Net, U-Net with an *EfficientNet* backbone, and *LinkNet* variants. SVM had the lowest IoU (0.67). L12-*LinkNet50* with heavy post-processing [SLIC superpixel and Dense Conditional Random Field

(CRF)] achieved the highest (0.91). RF, U-Net, and *EfficientNet* obtained IoUs of 0.68, 0.70, and 0.78, respectively. This study highlighted the importance of post-processing. Additionally, their findings demonstrated that combining multiple loss functions, such as *Lovász hinge* and *dice* loss, improves semantic segmentation performance.

Various studies have shown that DeepLab variants outperform U-Net. For instance, on a dataset that includes glacial lakes from the Third Pole region, Tang et al. (2024) compared multiple variants of DeepLab and U-Net. They found that DeepLabv3+ with a *MobileNetV3* (Howard et al., 2019) backbone achieved the highest performance (IoU: 0.95). Their evaluation included challenging conditions such as mountain shadows, frozen lakes, and wet ice. Other DeepLabv3+ variants with *ResNet50* (He et al., 2016), *Xception* (Chollet, 2017), and *MobileNetV2* (Sandler et al., 2018) backbones followed closely (IoU: 0.94). U-Net variants performed slightly lower: *ResNet50* (0.93), *MobileNetV3* (0.92), and *MobileNetV2* (0.92). The study demonstrated that for the same backbone, DeepLabv3+ consistently outperformed U-Net.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies > Table 2 > Small lake detection:
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. Their evaluation included challenging conditions such as mountain shadows , frozen lakes , and wet ice . Other DeepLabv3 + variants with * ResNet50 * ( He et al . , 2016 ) , * Xception * ( Chollet , 2017 ) , and * MobileNetV2 * ( Sandler et al . , 2018 ) backbones followed closely ( IoU : 0 . 94 ) . U - Net variants performed slightly lower : * ResNet50 * ( 0 . 93 ) , * MobileNetV3 * ( 0 . 92 ) , and * MobileNetV2 * ( 0 . 92 ) . The study demonstrated that for the same backbone , DeepLabv3 + consistently outperformed U - Net .

Siddique et al. (2023) also improved the IoU from 0.71 to 0.73 by switching to a DeepLabv3+ model (backbone: MobileNetV2) from U-Net (backbone: EfficientNetB0). U-Net's skip connections enable the fusion of low-level appearance details with high-level semantic features. This is important for accurately delineating lake boundaries, especially for small glacial lakes, because the skip connections preserve fine-grained spatial information by reintroducing high-resolution encoder features into the decoder (Fig. 7(a)). However, they can also propagate redundant information, potentially introducing noise irrelevant to glacial lake segmentation. In contrast, DeepLabv3+ employs ASPP, which applies parallel dilated convolutions at multiple scales to enhance multi-scale feature extraction (Fig. 7(b)). Each dilation rate captures features over a different receptive field size. This allows the model to recognize small lakes based on fine textures, large lakes based on broad spatial context, and lakes with complex geometries by combining structural information at multiple scales. This improves detection performance across a wide range of lake sizes and shapes.

A notable gap remains in ensuring fair and comprehensive comparisons among methodologies. Some ML/DL methods (e.g., Xu et al., 2023) have been compared only with thresholded spectral indices rather than against other state-of-the-art ML/DL approaches. In few cases (e.g., Dirscherl et al., 2020, 2021), there is no comparative analysis at all.

For a fair comparison, the approach being proposed should be evaluated at a study site where a high-performing method has already been tested. This is important because state-of-the-art algorithms might have been developed for entirely different regions. Alternatively, at least one such method may be implemented and evaluated on the region of interest. Despite the time and effort involved, such comparisons are essential for establishing the credibility of new approaches. Accordingly, they are strongly recommended.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies > Table 2 > Small lake detection:
Type: figure
Figure

Image /page/10/Figure/2 description: The image displays two related visualizations concerning evaluation metrics from scientific publications. The top part consists of seven circular charts, each representing a different metric, arranged around a central legend. The legend lists the metrics and their total counts: Recall (29) in blue, Confusion matrix (15) in orange, F1/Dice coefficient (26) in light green, Kappa (11) in purple, Precision (27) in brown, Overall accuracy (23) in gray, and IoU (22) in light yellow. Each circular chart is filled with labels representing publications (e.g., 'Wang\_22a', 'Dirscherl\_21'). The bottom part of the image is a bubble chart illustrating the yearly trend of these evaluation metrics from 2018 to 2024. The y-axis lists the metrics, and the x-axis shows the years. Each bubble is colored according to the metric and contains a number indicating the count for that year. The data from the bubble chart is as follows: Recall: 1 (2018), 3 (2020), 5 (2021), 6 (2022), 6 (2023), 8 (2024). Confusion matrix: 3 (2020), 3 (2021), 2 (2022), 3 (2023), 4 (2024). F1/Dice coefficient: 2 (2020), 5 (2021), 5 (2022), 7 (2023), 7 (2024). Kappa: 2 (2020), 3 (2021), 2 (2022), 3 (2023), 1 (2024). Precision: 1 (2018), 3 (2020), 5 (2021), 5 (2022), 5 (2023), 8 (2024). Overall accuracy: 1 (2018), 4 (2020), 3 (2021), 5 (2022), 6 (2023), 4 (2024). IoU: 1 (2020), 4 (2021), 4 (2022), 6 (2023), 7 (2024). A caption below the chart indicates this analysis is based on 41 publications.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 4. Learning-based approaches for glacial lake studies > Table 2 > Small lake detection:
Type: text

Fig. 6. Distribution (top) and yearly trend (bottom) of evaluation metrics based on 41 publications which reported detailed quantitative analysis. The legend indicates number of studies (in brackets) that used each metric, and circle radius reflects the usage frequency. Papers employing each metric are listed within respective circles.

A comprehensive comparison of top-performing methodologies on a fixed dataset is notably absent from the literature. Comparing *ACFNet* (Wang et al., 2021) with Cao et al. (2024)'s highest-performing *LinkNet* and Tang et al. (2024)'s best-performing DeepLabv3+ would provide valuable insights. Achieving this requires representative training datasets, along with rigorous cross-regional evaluations, model exchange, and standardized benchmarking protocols. Its findings could inform a truly generalizable ML/DL approach for global-scale glacial lake mapping and monitoring.

Ensuring reproducibility and transparency through open science practices is crucial for advancing intercomparisons in glacial lake research. Initiatives like NASA's Transform to OPen Science (TOPS, https://doi.org/10.5281/zenodo.10161527) lead the way in promoting open science. Some glacial lake studies have embraced this by making their code and/or data publicly accessible (Ortiz et al., 2022; Wang et al.,

2022a, etc.), promoting transparency. However, majority of studies still do not, making it difficult to access datasets and reproduce results, particularly for researchers or end-users with limited programming expertise.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 5. Discussion
Type: text

DL methods are preferred over classical ML for glacial lake studies, comprising nearly two-thirds of reported papers and more than 50% of the proposed methodologies (Figs. 2, 3). While DL outperforms ML with sufficient data, it is slower and less interpretable. In contrast, ML methods remain valuable for their computational efficiency, relatively simpler parameter tuning, and lower risk of overfitting.

The choice of model architecture significantly influences performance and adaptability across diverse glacial environments. Applying


Document: Monitoring earths glacial lakes from space with machine learning
Section: 5. Discussion
Type: figure
Figure

Image /page/11/Figure/2 description: A diagram illustrating the U-Net architecture, a type of convolutional neural network for image segmentation. The architecture is symmetrical and U-shaped, consisting of a contracting path (encoder) on the left and an expansive path (decoder) on the right. The input is a 572x572 image. The contracting path involves repeated blocks of two 3x3 convolutions followed by a 2x2 max pooling operation, progressively downsampling the image and increasing the number of feature channels (64, 128, 256, 512, 1024). The expansive path uses 2x2 up-convolutions to upsample the feature maps, concatenates them with corresponding feature maps from the contracting path via skip connections, and applies two 3x3 convolutions. This process progressively decreases the number of channels (1024, 512, 256, 128, 64). The final layer is a 1x1 convolution that produces a 388x388 output segmentation map with 2 channels. A legend in the bottom right corner explains the symbols used for different operations: 3x3 Conv, ReLU (green arrow), Skip connection (pink arrow), 2x2 Max pooling (dark red arrow), 2x2 Up-Conv (blue arrow), and 1x1 Conv (yellow arrow).


Document: Monitoring earths glacial lakes from space with machine learning
Section: 5. Discussion
Type: text

(a) An example U-Net architecture.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 5. Discussion
Type: figure
Figure

Image /page/11/Figure/4 description: A diagram illustrating an example U-Net architecture, divided into an Encoder and a Decoder section. The process begins with an 'Input image' fed into the Encoder. Inside the Encoder, the image passes through a 'Backbone network' block labeled 'Atrous convolution'. The output of this block splits. One path, labeled 'Low-level Features', goes directly to the Decoder. The other path enters a module with five parallel operations: '1x1 Conv', '3x3 Conv rate 6', '3x3 Conv rate 12', '3x3 Conv rate 18', and 'Image Pooling'. The outputs of these five operations are concatenated and then passed through a '1x1 Conv' block. The result from this block is then sent to the Decoder. In the Decoder section, the features from the Encoder's main path are first processed by an 'Upsample by 4' block. Concurrently, the 'Low-level Features' from the Encoder are processed by a '1x1 Conv' block. The outputs of both these paths are then combined using a 'Concat' block. This concatenated output is passed through a '3x3 Conv' block, followed by another 'Upsample by 4' block, which produces the final 'Output segmentation map'.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 5. Discussion
Type: text

(b) DeepLabv3+ architecture.

Fig. 7. Architectures of key models. Conv, ReLU, and concat stand for convolution, Rectified Linear Unit, and concatenation respectively.

derivatives of U-Net or DeepLab architectures across different study sites is acceptable, as the primary focus is on studying glacial lake dynamics rather than model exploration. However, this approach prioritizes exploitation over exploration, potentially limiting the discovery of novel methods better suited to diverse environmental conditions.

The research community should embrace underexplored architectures, including those successfully applied in other domains of Earth science. For instance, Recurrent Neural Network (RNN)s, including Long Short-Term Memory (LSTM) networks (Sherstinsky, 2020; Hochreiter and Schmidhuber, 1997), are not recent innovations. However, their ability to capture fine-grained time-series dynamics (Ismail Fawaz et al., 2019) remains largely untapped in glacial lake studies. No research has applied these models to analyze temporal lake evolution. Current approaches rely on independent per-image predictions followed by simple multi-temporal analyses. RNNs typically require longer training times than CNNs. However, their potential for recognizing temporal dynamics in glacial lake monitoring warrants further exploration.

A round-robin comparison of the best-performing methods would provide a clearer picture of the trade-offs between computational complexity and performance gains. Including a comprehensive costbenefit analysis would strengthen this comparison. Together, these efforts could better guide future methodological improvements.

A key challenge is the impact of pre-processing choices, such as at-mospheric correction, on performance. Despite its computational cost,

the extent to which atmospheric correction improves model accuracy remains largely unquantified. Assessing its effect on classification performance could optimize processing pipelines for large-scale glacial lake studies.

The relevance of ML/DL approaches depends on the application domain. These methods are valuable for regional-scale research where large numbers of lakes need to be analyzed efficiently. However, their utility is limited for site-specific applications – such as hydropower development in discrete or transboundary river basins – where field validation, in situ monitoring, and physically-based hazard modeling are typically required.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 6. Conclusion and outlook
Type: text

Glacial lakes are rapidly expanding due to climate change-driven glacier retreat, increasing the likelihood of outburst floods and impacting human lives and infrastructure. Monitoring these lakes is crucial for assessing hazards, improving early warning systems, and managing freshwater availability. However, effective large-scale studies remain a challenge due to the remoteness of glacial lakes and limited in situ data. Learning-based approaches, particularly deep learning, have emerged as powerful tools, offering numerous opportunities for automating glacial lake mapping and monitoring. However, their full potential remains underexplored.

This paper reviews the existing literature on classical machine learning and deep learning methodologies for remote sensing of glacial lakes. It surveys 48 studies, outlines their respective strengths and weaknesses, identifies key research gaps, and provides best practice recommendations.

Most studies have focused on glacial lakes in Asia, Greenland, and Antarctica. Critical regions such as the Andes and European Alps have received limited attention. Optical sensors, particularly Landsat-8 and Sentinel-2, are widely used, complemented by Sentinel-1 radar data. Optical-only approaches have shown success in polar and mountainous regions but rely heavily on the pre-selection of cloud-free images. Radar-only approaches, on the other hand, have been successful in polar lowlands but are yet to prove effective in mountainous regions. Although radar data mitigates cloud-related issues, it requires extensive pre-processing to be analysis-ready. While both single- and multi-sensor approaches have been explored, multi-sensor fusion - especially SAR-optical combinations - has proven more effective. Despite these advances, mapping and monitoring glacial lakes in mountainous terrain under cloudy conditions remains an unresolved challenge. Furthermore, detecting lakes smaller than 0.01 km2 remains challenging, primarily due to sensor resolution rather than methodological

Deep learning methods have demonstrated significant success in glacial lake studies. Convolutional neural networks, particularly U-Net, DeepLab, and their variants, have emerged as prominent approaches. More deep learning approaches have been published compared to classical machine learning and continue to be increasingly reported. Among machine learning methods, random forests and support vector machines have been widely explored. A strong preference for pixel-wise supervised classification is evident. Weakly supervised learning approaches remain underexplored, despite their potential to reduce reliance on extensive ground truth labels. Transfer learning and data augmentation have significantly alleviated the bottleneck of limited labeled data availability. Consequently, training parameterintensive deep learning models has become more feasible. Nonetheless, high computational costs continue to limit their broader adoption in resource-constrained settings.

To advance robust and scalable glacial lake mapping and monitoring, learning-based approaches must meet several key criteria. High accuracy on primary study sites is essential. Equally important is the ability to generalize across space and time. Ideally, models should adapt to new study regions with minimal retraining. They should also accurately capture intra-annual lake dynamics.

However, several challenges and opportunities remain. Relatively few studies investigate the transferability of their models across study sites and time periods. This raises concerns about generalizability, especially since data-driven approaches are susceptible to overfitting on less representative datasets. Rigorous spatiotemporal transferability experiments with both quantitative and qualitative evaluations must be prioritized.


Document: Monitoring earths glacial lakes from space with machine learning
Section: 6. Conclusion and outlook
Type: text

glacial lake mapping and monitoring , learning - based approaches must meet several key criteria . High accuracy on primary study sites is essential . Equally important is the ability to generalize across space and time . Ideally , models should adapt to new study regions with minimal retraining . They should also accurately capture intra - annual lake dynamics . However , several challenges and opportunities remain . Relatively few studies investigate the transferability of their models across study sites and time periods . This raises concerns about generalizability , especially since data - driven approaches are susceptible to overfitting on less representative datasets . Rigorous spatiotemporal transferability experiments with both quantitative and qualitative evaluations must be prioritized .

Moreover, many studies focused on seasonal mapping of glacial lakes rather than year-round monitoring of their temporal evolution. This limits the ability to track intra-annual variations. Future studies should shift focus to multi-temporal analyses. Expanding training datasets with reference data spanning multiple seasons and years is equally important. Additionally, emerging deep learning models capable of learning seasonal and inter-annual variations need to be leveraged to capture dynamic glacial lake evolution.

Existing learning-based approaches, especially deep learning, while effective, operate as black-box models that may violate hydrological and glaciological constraints. Integrating physical principles into data-driven models offers a compelling opportunity to enhance interpretability, explainability and ensure physical consistency. By aligning predictions with known physical behaviors and ensuring physically plausible outputs, unrealistic extrapolations in poorly observed regions can be prevented.

For fair and transparent performance evaluation, future studies should employ robust evaluation metrics that do not overlook class imbalance and stick to standardized benchmarking protocols. High-resolution (spatial) imagery (e.g., PlanetScope, Pléiades, UAV) may be used to improve detection of lakes smaller than 0.01 km2. However, the associated costs and limited scalability must be considered. Research should be expanded to underrepresented regions. Open-sourcing datasets, ground truth labels, and code could further advance learning-based glacial lake monitoring from space.


Document: Monitoring earths glacial lakes from space with machine learning
Section: CRediT authorship contribution statement
Type: text

Manu Tom: Writing – review & editing, Writing – original draft, Visualization, Investigation, Conceptualization. Daniel Odermatt: Writing – review & editing, Supervision, Project administration, Funding acquisition. Cédric H. David: Writing – review & editing, Funding acquisition. Arnaud Cerbelaud: Writing – review & editing. Jeffrey Wade: Writing – review & editing. Holger Frey: Writing – review & editing, Supervision, Project administration, Funding acquisition.


Document: Monitoring earths glacial lakes from space with machine learning
Section: Declaration of Generative AI and AI-assisted technologies in the writing process
Type: text

During the preparation of this work the lead author used Chat-GPT 40 to improve the readability and language of the first draft manuscript. After using this tool/service, all authors reviewed and edited the manuscript and take full responsibility for the content of the publication.


Document: Monitoring earths glacial lakes from space with machine learning
Section: Funding sources
Type: text

This work was supported by the *GLOFCA*1 project funded by the Adaptation Fund and implemented by UNESCO Almaty office and the University of Zurich; and the *Alpglacier*2 project funded by European Space Agency (Grant nr: 4000133436/20/INB). Manuscript preparation was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004) including a grant from NASA's Terrestrial Hydrology Program.
© 2025. All rights reserved.


Document: Monitoring earths glacial lakes from space with machine learning
Section: Appendix A. Remote sensing data used
Type: text

See Table A.3.

http://glofca.org/.

<sup>2 https://alpglacier.geo.uzh.ch/.


Document: Monitoring earths glacial lakes from space with machine learning
Section: Appendix A. Remote sensing data used
Type: table
Table: Table A.3 Overview of satellite imagery and topographic data used in learning-based glacial lake studies.

| Category                     | Sensor (count)     | Publication(s)                                                                                |
|------------------------------|--------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Optical satellite imagery | Landsat-8 (22)     | Veh et al. (2018), Halberstadt et al. (2020), Yuan et al. (2020), Wu et al. (2020), He et al. (2021), Wang et al. (2021), Thati et al. (2021), Zhao et al. (2021), Chen et al. (2022), Dell et al. (2022), Kaushik et al. (2022), Thati and Ari (2022), Wang et al. (2022a,c), Banerjee and Bhuiyan (2023), Sharma and Prakash (2023), Zhao et al. (2023), Hardie et al. (2024), Sharma et al. (2024), Tang et al. (2024), Wang and Sugiyama (2024) and Xu et al. (2024) |
|                              | Sentinel-2 (20)    | Dirscherl et al. (2020), Wangchuk and Bolch (2020), Rinzin et al. (2021), Dirscherl et al. (2021), Wendleder et al. (2021), Basit et al. (2022), Chatterjee et al. (2022), Hu et al. (2022), Kaushik et al. (2022), Ortiz et al. (2022), Lutz et al. (2023), Niu et al. (2023), Wei et al. (2023), Xu et al. (2023), Hu et al. (2024), Mustafa et al. (2024), Wang and Sugiyama (2024), Wu et al. (2024), Xu et al. (2024) and Yin et al. (2024)                         |
|                              | Planetscope (5)    | Qayyum et al. (2020), Wendleder et al. (2021), Siddique et al. (2023), Thomas et al. (2023) and Xu et al. (2024)                                                                                |
|                              | Landsat-Other (2)  | Veh et al. (2018) and Banerjee and Bhuiyan (2023)                                                                                |
|                              | Landsat-7 (1)      | Banerjee and Bhuiyan (2023)                                                                                |
|                              | ASTER (1)          | Jain et al. (2015)                                                                                |
|                              | Corona KH-4 (1)    | Rinzin et al. (2021)                                                                                |
|                              | IRS LISS III (1)   | Sharma and Prakash (2023)                                                                                |
| Radar satellite imagery   | Sentinel-1 (15)    | Wangchuk and Bolch (2020), Wu et al. (2020), Zhang et al. (2020b), Dirscherl et al. (2021), How et al. (2021), Rinzin et al. (2021), Wang et al. (2021), Jiang et al. (2022), Kaushik et al. (2022), Wendleder et al. (2021), Xu et al. (2023), Hu et al. (2024), Mustafa et al. (2024), Wu et al. (2024) and Xu et al. (2024)                                                                                |
|                              | TerraSAR-X (1)     | Wendleder et al. (2021)                                                                                |
|                              | GaoFen-3 (1)       | Chen (2021)                                                                                |
| DEM                          | SRTM (7)           | Veh et al. (2018), Wangchuk and Bolch (2020), Rinzin et al. (2021), Wang et al. (2022c), Banerjee and Bhuiyan (2023), Mustafa et al. (2024) and Yin et al. (2024)                                                                                |
|                              | ArcticDEM (5)      | How et al. (2021), Hu et al. (2022), Lutz et al. (2023), Wei et al. (2023) and Wang and Sugiyama (2024)                                                                                |
|                              | ASTER (3)          | Sharma and Prakash (2023), Hardie et al. (2024) and Sharma et al. (2024)                                                                                |
|                              | ALOS (3)           | Veh et al. (2018), Kaushik et al. (2022) and Xu et al. (2023)                                                                                |
|                              | NASADEM (2)        | Chen et al. (2022) and Hu et al. (2024)                                                                                |
|                              | TanDEM-X (2)       | Dirscherl et al. (2020, 2021)                                                                                |
|                              | Copernicus DEM (1) | Xu et al. (2024)                                                                                |


Document: Monitoring earths glacial lakes from space with machine learning
Section: Appendix B. Search methodology for literature review
Type: text

To ensure a comprehensive review of glacial lake remote sensing studies that used ML/DL, we conducted a structured literature search using *web of science* and *google scholar*. We included all relevant studies published through the end of 2024 (inclusive).

For web of science, we used the search string: [glacial lakes OR glacier lakes] AND [deep learning OR machine learning].

In google scholar, we used the following keyword combinations: glacial lakes deep learning, glacial lakes machine learning, glacier lakes deep learning, glacier lakes machine learning, proglacial lakes, ice-dammed lakes, supraglacial lakes, glacial lakes, and glacier lakes.

During the selection process, we first screened titles and abstracts to filter out irrelevant studies. We prioritized peer-reviewed journal articles and conference proceedings. Although *google scholar* offers an extensive search scope, it also returns non-peer-reviewed sources (e.g., preprints), which were excluded. ML/DL papers focusing on general glacier dynamics without addressing glacial lakes were also excluded. Finally, we thoroughly examined the reference lists of shortlisted papers to identify any additional relevant studies.


Document: Monitoring earths glacial lakes from space with machine learning
Section: Appendix C. Methodology distribution
Type: text

See Table C.4.

Appendix D. Variants of U-Net and Deeplab

See Table D.5.

Appendix E. Regional distribution

See Table E.6.

Appendix F. Loss functions and optimization strategies used

See Tables F.7 and F.8.


Document: Monitoring earths glacial lakes from space with machine learning
Section: Appendix G. Glossary
Type: text

AdaM Adaptive Moment.

AI Artificial Intelligence.

ANN Artificial Neural Network.

ASPP Atrous Spatial Pyramid Pooling.

C-K-MEANS Cascaded K-Means.

CNN Convolutional Neural Network.

CRT Classification and Regression Trees.

**DCNN** Deep Convolutional Neural Network.

**DELSE** DEep Level Set Evolution.

**DEM** Digital Elevation Model.

DL Deep Learning.

EBT Ensemble-Bagged Trees.

FPN Feature Pyramid Network.

GAN Generative Adversarial Network.

GLOF Glacial Lake Outburst Flood.

HMA High Mountain Asia.

IoU Intersection-over-Union.

KNN K-Nearest Neighbors.


Document: Monitoring earths glacial lakes from space with machine learning
Section: Appendix G. Glossary
Type: table
Table: Table C.4 Overview of ML and DL methods used for glacial lake remote sensing.

|         | Methodology [count]                           | Publication(s)                                                                                |
|---------|-----------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| ML [31] | RF [12]                                       | Veh et al. (2018), Dirscherl et al. (2020), Halberstadt et al. (2020), Wangchuk and Bolch (2020), Rinzin et al. (2021), Wendleder et al. (2021), Chen et al. (2022), Dell et al. (2022), Hu et al. (2022), Banerjee and Bhuiyan (2023), Mustafa et al. (2024) and Wang and Sugiyama (2024)                                                                                |
|         | SVM [4]                                       | Jain et al. (2015), Halberstadt et al. (2020), Zhang et al. (2020b) and Mustafa et al. (2024)                                                                                |
|         | K-MEANS+ [3]                                  |                                                                                |
|         | K-MEANS [2]                                   | Thati et al. (2021) and Wu et al. (2024)                                                                                |
|         | Cascaded K-Means (C-K-MEANS) [1]              | Wu et al. (2024)                                                                                |
|         | Artificial Neural Network (ANN) [2]           | Banerjee and Bhuiyan (2023) and Mustafa et al. (2024)                                                                                |
|         | Others [10]                                   |                                                                                |
|         | Logistics Regression (LR) [1]                 | Mustafa et al. (2024)                                                                                |
|         | Maximum Entropy (ME) [1]                      | Halberstadt et al. (2020)                                                                                |
|         | Naive Bayes (NB) [1]                          | Halberstadt et al. (2020)                                                                                |
|         | Classification and Regression Trees (CRT) [1] | Halberstadt et al. (2020)                                                                                |
|         | Minimum Distance (MD) [1]                     | Halberstadt et al. (2020)                                                                                |
|         | Ensemble-Bagged Trees (EBT) [1]               | How et al. (2021)                                                                                |
|         | ISODATA [1]                                   | Thati et al. (2021)                                                                                |
|         | Learning Vector Quantization (LVQ) [1]        | Wu et al. (2024)                                                                                |
|         | K-Nearest Neighbors (KNN) [1]                 | Mustafa et al. (2024)                                                                                |
|         | X-MEANS [1]                                   | Wu et al. (2024)                                                                                |
| DL [39] | CNN Variants [37]                             |                                                                                |
|         | U-Net [18]                                    | Qayyum et al. (2020), Wu et al. (2020), Chen (2021), Dirscherl et al. (2021), He et al. (2022), Basit et al. (2022), Jiang et al. (2022), Ortiz et al. (2022), Thati and Ari (2022), Wang et al. (2022a), Lutz et al. (2023), Niu et al. (2023), Sharma and Prakash (2023), Siddique et al. (2023), Wei et al. (2023), Hu et al. (2024), Sharma et al. (2024) and Tang et al. (2024) |
|         | DeepLab Variants [6]                          | Siddique et al. (2023), Xu et al. (2023), Hardie et al. (2024), Sharma et al. (2024), Tang et al. (2024) and Xu et al. (2024)                                                                                |
|         | CNN [2]                                       | Yuan et al. (2020) and Thomas et al. (2023)                                                                                |
|         | LinkNet [2]                                   | Thati and Ari (2022) and Cao et al. (2024)                                                                                |
|         | Others [9]                                    |                                                                                |
|         | ACFNet [1]                                    | Wang et al. (2021)                                                                                |
|         | Mask-R-CNN [1]                                | Chatterjee et al. (2022)                                                                                |
|         | Feature Pyramid Network (FPN) [1]             | Thati and Ari (2022)                                                                                |
|         | HarDNet (Second-order Attention Network) [1]  | Wang et al. (2022c)                                                                                |
|         | Siamese CNN [1]                               | Zhao et al. (2023)                                                                                |
|         | Deep Convolutional Neural Network (DCNN) [1]  | Kaushik et al. (2022)                                                                                |
|         | You Only Look Once (YOLO) [1]                 | Yin et al. (2024)                                                                                |
|         | CoAtNet [1]                                   | Xu et al. (2023)                                                                                |
|         | PSPNet [1]                                    | Thati and Ari (2022)                                                                                |
|         | Others [2]                                    |                                                                                |
|         | Generative Adversarial Network (GAN) [1]      | Zhao et al. (2021)                                                                                |
|         | DELSE [1]                                     | Ortiz et al. (2022)                                                                                |


Document: Monitoring earths glacial lakes from space with machine learning
Section: Appendix G. Glossary
Type: text

L8 Landsat-8.

LR Logistics Regression.

LVQ Learning Vector Quantization.

MD Minimum Distance.

ME Maximum Entropy.

ML Machine Learning.

MLE Maximum Lake Extent.

MNDWI Modified Normalized Difference Water Index.

NB Naive Bayes.

NDWI Normalized Difference Water Index.

NIR Near InfraRed.

PPC Per-Pixel Classification.

RF Random Forest.

RNN Recurrent Neural Network.

**S1** Sentinel-1.

S2 Sentinel-2.

SAR Synthetic Aperture Radar.

SGD Stochastic Gradient Descent.

SVM Support Vector Machine.

SWOT Surface Water and Ocean Topography.

ToA Top of Atmosphere.

UAV Unmanned Aerial Vehicle.

YOLO You Only Look Once.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Type: text

Changjun Gu, Suju Li\*, Ming Liu, Kailong Hu and Ping Wang

National Disaster Reduction Center, Ministry of Emergency Management, Beijing 100124, China \* Correspondence: lisuju@ndrcc.org.cn

Abstract: Establishing an effective real-time monitoring and early warning system for glacier lake outburst floods (GLOFs) requires a full understanding of their occurrence mechanism. However, the harsh conditions and hard-to-reach locations of these glacial lakes limit detailed fieldwork, making satellite imagery a critical tool for monitoring. Lake Mercbacher, an ice-dammed lake in the central Tian Shan mountain range, poses a significant threat downstream due to its relatively high frequency of outbursts. In this study, we first monitored the daily changes in the lake area before the 2022 Lake Mercbacher outburst. Additionally, based on historical satellite images from 2014 to 2021, we calculated the maximum lake area (MLA) and its changes before the outburst. Furthermore, we extracted the proportion of floating ice and water area during the period. The results show that the lake area of Lake Mercbacher would first increase at a relatively low speed (0.01 km2/day) for about one month, followed by a relatively high-speed increase (0.04 km2/day) until reaching the maximum, which would last for about twenty days. Then, the lake area would decrease slowly until the outburst, which would last five days and is significant for early warning. Moreover, the floating ice and water proportion provides more information about the outburst signals. In 2022, we found that the floating ice area increased rapidly during the early warning stage, especially one day before the outburst, accounting for about 50% of the total lake area. Historical evidence indicates that the MLA shows a decreasing trend, and combining it with the outburst date and climate data, we found that the outburst date shows an obvious advance trend (6 days per decade) since 1902, caused by climate warming. Earlier melting results in an earlier outburst. This study provides essential references for monitoring Lake Mercbacher GLOFs and building an effective early warning system.

Keywords: Lake Mercbacher; GLOF; remote sensing; early warning; climate change

data/('Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images', '.pdf')_extraction.md


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Type: figure
Figure

Image /page/0/Picture/8 description: A graphic on a white background featuring a yellow circular icon with a white checkmark inside. To the right of the icon, the text 'check for updates' is displayed in two lines. The top line reads 'check for' in a regular dark gray font, and the bottom line reads 'updates' in a bold dark gray font.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Type: text

Citation: Gu, C.; Li, S.; Liu, M.; Hu, K.; Wang, P. Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images. *Remote Sens.* **2023**, *15*, 1941. https://doi.org/10.3390/rs15071941

Academic Editor: Yi Luo

Received: 25 February 2023 Revised: 3 April 2023 Accepted: 4 April 2023 Published: 5 April 2023


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Type: figure
Figure

Image /page/0/Picture/12 description: A Creative Commons Attribution (CC BY) license icon. The icon is a rectangle with a grey top section and a black bottom section. The grey section contains two symbols in white circles with black outlines. On the left is the Creative Commons 'CC' symbol, and on the right is the attribution symbol, which is a stylized icon of a person. In the black section below the attribution symbol, the letters 'BY' are written in white.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Type: text

Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 1. Introduction
Type: text

Glacial deglaciation is becoming a worldwide phenomenon due to accelerating global warming [1–4]. As glaciers recede, their stability decreases, and they release stored water, which can lead to glacial hazards [5–7]. The melting water of glaciers, combined with overdeepenings in former glacier beds, has caused the global glacier lake volume and quantity to increase rapidly [8–10]. In fact, it has been suggested that the lakes formed from melting glaciers have increased by 50% in just 30 years (1990–2018). Dammed by abandoned moraines and ice, glacier lakes are susceptible to outbursts and can cause severe damage to human settlements and infrastructure downstream [7,11–15]. Statistics show more than 6300 people have died of glacier lake outburst floods (GLOFs) since the 1500s in High Mountain Asia alone [14]. Moreover, 15 million people globally are exposed to impacts from potential GLOFs, especially in High Mountains Asia (HMA) [13]. Real-time monitoring and early warning systems can effectively forecast active and high-frequency floods resulting from ice dam breaches [9]. However, GLOFs dynamics are challenging to measure and quantify due to unpredictable triggers and hard-to-reach locations [16].

Remote Sens. 2023, 15, 1941 2 of 18

Fortunately, increasingly detailed digital topographic data and satellite imagery provide more possibilities in GLOF monitoring [17–19].

Located in the upper reaches of the Kumarik River in Kyrgyzstan, a tributary of the Aksu River in China, Lake Merzbacher is the largest glacial lake in the central Tien Shan mountain range of northwestern HMA [19]. The lake derives its water from the melt of the Inylchek Glacier and is divided into two parts: an upper lake and a lower lake [20]. The lower lake is ice-dammed and much larger than the upper one during the warm season when it is filled with water. Studies and reports have shown that there were 80 recorded outbursts of lower lake from 1932 to 2021 [21]. The average elevation of the lower lake surface is about 3380 m, and the drop from the lake to 200 km far away downstream communities is approximately 2000 km. This substantial drop in elevation makes Lake Merzbacher outburst floods to be more destructive and pose a significant threat to human settlements and infrastructure downstream.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 1. Introduction
Type: text

divided into two parts : an upper lake and a lower lake [ 20 ] . The lower lake is ice - dammed and much larger than the upper one during the warm season when it is filled with water . Studies and reports have shown that there were 80 recorded outbursts of lower lake from 1932 to 2021 [ 21 ] . The average elevation of the lower lake surface is about 3380 m , and the drop from the lake to 200 km far away downstream communities is approximately 2000 km . This substantial drop in elevation makes Lake Merzbacher outburst floods to be more destructive and pose a significant threat to human settlements and infrastructure downstream .

Lake Merzbacher, first discovered by Gottfried Merzbacher in 1903 [22], has been the focus of research on its formation and outburst mechanism since recording its first outburst flood in 1931. The mystery of the outburst mechanism was not solved until Ajrapet demonstrated the process in 1971, revealing that the northern Inylchek Glacier dammed the lake, and a hidden stream existed under the ice dam. Lake Merzbacher exhibits a unique outburst mechanism that differs from other glacial lakes. Research has shown that the northern Inylchek Glacier dams the lake, and a hidden stream flows beneath the ice dam. As the lake fills with water, the dam gradually rises, and water flows downstream through the hidden stream [23]. This process provides a short window of opportunity for early warning. Previous studies have explored the potential methods to capture this signal. An understanding of the outburst probability is central to early warning, and thus predicting the outburst probability of GLOFs draws much academic attention. By selecting the main factors which link to dam failure, the qualitative probability of dam failure was derived and mapped over a large region [24,25]. However, identifying potentially dangerous lakes is crucial to early warning efforts. Xie et al. [19] first build an index to monitor the outburst of Lake Merzbacher based on time series remote sensing images before and after the flood. Nonetheless, the temporal-spatial resolution of available satellite images can hardly capture all the details before the outburst of Lake Merzbacher. One possible scenario is that there would not be enough satellite shreds of evidence before the outburst, especially considering the frequent clouds in this region. Therefore, continuous monitoring of the lake before an outburst is vital for the timely and accurate forecast of disasters.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 1. Introduction
Type: text

, 25 ] . However , identifying potentially dangerous lakes is crucial to early warning efforts . Xie et al . [ 19 ] first build an index to monitor the outburst of Lake Merzbacher based on time series remote sensing images before and after the flood . Nonetheless , the temporal - spatial resolution of available satellite images can hardly capture all the details before the outburst of Lake Merzbacher . One possible scenario is that there would not be enough satellite shreds of evidence before the outburst , especially considering the frequent clouds in this region . Therefore , continuous monitoring of the lake before an outburst is vital for the timely and accurate forecast of disasters .

Research has identified four distinct periods in the outburst process of Lake Merzbacher during a GLOF: icefall, quick water storage, early warning, and post-drainage. However, the characteristics and duration of the four periods are still unclear. One chief reason for this is that there are not enough available images. Glacial lakes are often located in alpine regions, where the cloudy weather decreased the availability of the time series satellite images. Furthermore, widely used satellite images, including MODIS, Landsat, and Sentinel, are deficient for the long revisit period and thus can hardly support GLOF monitoring. During the past twenty years, China's Earth observation activities have rapidly developed, creating a spaceship fleet resembling those from NASA, ESA, or JAXA, forming Chinese Earth Observation Satellite (CEOS) constellations [26,27]. Five major CEOS constellations are ready for use, including GaoFen satellites and HuanjingJianzai satellites, which have been widely used in land cover mapping [28–31]. The high resolution and short revisit period of (CEOS) constellations provide more opportunities in time series remote sensing monitoring. This study aims to obtain a comprehensive record of the Lake Merzbacher outburst using dense Chinese satellite images, and to analyze key elements during the outburst flood process, ultimately quantifying warning signals.

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Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 2. Materials and Methods > 2.1. Study Area
Type: text

Lake Merzbacher, located in the Inylchek Region at 79°52′E and 42°13′N, is situated in the central Tien Shan mountain range in Kyrgyzstan and close to the Chinese border [19,20]. It is considered the largest moraine lake in Central Asia, which was formed by Inylchek Glacier and first discovered and named by the German explorer Gottfried Merzbacher in 1903 [32]. Specifically, the northern branch of the Inylchek Glacier, with a length of 41 km and an area of 247 km², retreat and the southern branch of the Inylchek Glacier, with a length of 61 km and an area of 574 km², expansion together formed this glacier lake (Figure 1). This glacial lake plays a vital role in preventing the direct runoff of North Inylchek Glacier drainage water into the valley [33]. Lake Merzbacher comprises two small lakes connected by the Merzbacher River [32]: the upper one at 3400 m and the lower one at 3300 m. Lower Lake Merzbacher, dammed by the Southern Inylchek Glacier and larger than Upper Lake Merzbacher, is responsible for the glacier lake outburst flood (GLOF) that occurs regularly and has shown a decreasing trend since the beginning of the 20th century [34].

2. Materials and Methods
2.1. Study Area


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 2. Materials and Methods > 2.1. Study Area
Type: figure
Figure

Image /page/2/Figure/4 description: A side-by-side comparison of two maps of a mountainous region. The left panel is a topographic map showing the border between Kyrgyzstan and China. It labels Merzbacher Lake with a red dot, the Kumarik River, and a hydropower station with a red triangle. A legend explains the symbols and provides an elevation scale from 1500 to 5000 meters. The coordinates range from 79°0'E to 79°50'E longitude and 41°20'N to 42°20'N latitude. The right panel is a satellite image focusing on the glacial area. It labels the Northern Inylcheck Glacier, the Southern Inylcheck Glacier, an Upper Lake, and a Lower Lake. The glaciers are shown in white and light blue. The coordinates for this panel range from 79°49'E to 79°56'E longitude and 42°9'N to 42°15'N latitude. Each map includes a scale bar.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 2. Materials and Methods > 2.1. Study Area
Type: figure
Figure: Figure 1. The location of lake Merzbacher.

Figure 1. The location of lake Merzbacher.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 2. Materials and Methods > 2.1. Study Area
Type: text

Located in the upper reaches of Kunmaalik River in Aksu Prefecture, Xinjiang Uygur Autonomous Region, Lake Merzbacher's GLOF poses a significant threat to the downstream inhabitants. The lake's primary water source is from the melting of glaciers and snow during the ablation season, starting in early March and ending in September due to the semi-continental climate. This is precisely why glacial lake outburst floods (GLOFs) occur more frequently during this season [35]. To monitor the Lake Merzbacher GLOF, Xiehela hydrographic station was established in 1956, which is downstream of the lake. Since then,

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outburst records have occurred almost every year. After extensive research by predecessors, the mechanism behind GLOFs is no longer a secret [36]. When the ice dam of a glacial lake lifts due to a continuous increase in water, the lake enters the drainage process. The concealed water channels beneath the dam open up, connecting the lake water with the outside world, and the lake water is rapidly drained.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 2. Materials and Methods > 2.2. Data Sources
Type: text

Satellite data: All the satellite images were obtained from China Centre for Resources Satellite Data and Application, Beijing, China, (https://data.cresda.cn/#/home, accessed on 24 February 2023), except for Beijing-2 (BJ-2), which was obtained from Twenty First Century Aerospace Technology Co., Ltd., Beijing, China (https://www.21at.com.cn/, accessed on 24 February 2023) (Table 1).

2. Materials and Methods
2.2. Data Sources


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 2. Materials and Methods > 2.2. Data Sources
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Table

| <b>Table 1.</b> Satellite ima | ges from 2014 to 2022 |
|-------------------------------|-----------------------|
|-------------------------------|-----------------------|

Table 1. Satellite ima
ges from 2014 to 2022


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 2. Materials and Methods > 2.2. Data Sources
Type: table
Table

| Date           | Sensor    | Resolution | Date                      | Sensor     | Resolution |
|----------------|-----------|------------|---------------------------|------------|------------|
| 1 August 2014  | GF-1 WFV1 | 16 m       | 29 July 2021              | GF-1B_PMS  | 2/8 m      |
| 10 August 2014 | GF-1 WFV3 | 16 m       | 18 May 2022               | HJ-2A_CCD2 | 16 m       |
| 12 July 2015   | GF-1 WFV3 | 16 m       | 28 May 2022               | HJ-2A_CCD3 | 16 m       |
| 9 August 2015  | GF-1 WFV1 | 16 m       | 3 June 2022               | HJ-2B_CCD3 | 16 m       |
| 25 June 2016   | GF-1 WFV4 | 16 m       | 11 June 2022              | GF-1C_PMS  | 2/8 m      |
| 2 July 2016    | GF-1 WFV1 | 16 m       | 17 June 2022              | HJ-2A_CCD4 | 16 m       |
| 6 June 2017    | GF-1 WFV2 | 16 m       | 21 June 2022/ 8 July 2022 | HJ-2A_CCD4 | 16 m       |
| 28 July 2017   | GF-1 WFV4 | 16 m       | 12 July 2022              | GF-1B_PMS  | 2/8 m      |
| 6 July 2018    | GF-1 WFV2 | 16 m       | 13 July 2022              | GF-1B_PMS  | 2/8 m      |
| 10 July 2018   | GF-1 WFV1 | 16 m       | 14 July 2022              | GF-1C_PMS  | 2/8 m      |
| 19 July 2018   | GF-1 WFV3 | 16 m       | 14 July 2022              | HJ-2B_CCD3 | 16 m       |
| 4 August 2018  | GF-1 WFV2 | 16 m       | 15 July 2022              | GF-1D_PMS  | 2/8 m      |
| 13 August 2018 | GF-1 WFV4 | 16 m       | 16 July 2022              | HJ2A_CCD3  | 16 m       |
| 5 July 2019    | GF-1D PMS | 2/8 m      | 17 July 2022              | GF1_WFV2   | 2 m        |
| 23 July 2019   | GF-1 WFV3 | 16 m       | 18 July 2022              | BJ-2       | 0.8 m      |
| 5 August 2020  | GF-6 PMS  | 2/8 m      | 20 July 2022              | HJ2A_CCD4  | 16 m       |
| 3 July 2021    | GF-6 WFV  | 16 m       |                           |            |            |

Date Sensor Resolution Date Sensor Resolution


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 2. Materials and Methods > 2.2. Data Sources
Type: text

Gaofen-1 (GF-1): The GF-1 satellite, the first satellite in the China high-resolution earth observation system, was launched on 26 April 2013, and was put into use on 30 December 2013 [37]. Two high-resolution (HR) cameras (2 m resolution panchromatic/8 m resolution multispectral cameras) and four wide field of view (WFV) cameras (16 m resolution multispectral cameras) were equipped with GF-1, which have four spectral channels (blue, green, red) distributed in the visible and NIR spectral domain ranging from 450 to 890 nm (Table 2) [38]. The repetition cycle of GF-1 WFV data can reach four days by combining the four cameras.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 2. Materials and Methods > 2.2. Data Sources
Type: table
Table: Table 2. Technical parameter of GaoFen-1 (GF-1) (wide field of view (WFV) and panchromatic/multispectral high resolution (PMS)).

| Payload | Spectral Type                                      | Spectral Range (nm)                              | Spatial Resolution (m) | Swath Width (km)       | Revisit Interval       |
|---------|-------------------------------------------------------|-----------------------------------------------------|------------------------------|---------------------------|---------------------------|
| WFV     | Blue Green Red Near infrared                 | 450~520 520~590 630~690 770~890            | 16                           | 800 (Set of 4 cameras) | Four days (side-swing) |
| PMS     | Panchromatic Blue Green Red Near infrared | 450~900 450~520 520~590 630~690 770~890 | 2 8                       | 60 (Set of 2 cameras)  |                           |

Payload
Spectral Type
Spectral Range (nm)
Spatial Resolution (m)
Swath Width (km)
Revisit Interval


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 2. Materials and Methods > 2.2. Data Sources
Type: text

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Gaofen-6 (GF-6): The GF-6 satellite was launched on 2 June 2018, and was officially put into use on 21 March 2019. GF-6 is a low-orbit optical satellite equipped with a panchromatic/multispectral high-resolution (PMS) camera and a wide field view (WFV) camera (Table 3) [39,40]. This study used the GF-6 WFV and PMS images obtained on 5 August 2020 and 13 July 2021. GF-6/WFV with 16 m resolution has 8 bands, except for one coastal band (400~450 nm) and three visible bands (450~520 nm, 520~590 nm, and 630~690 nm). Same as GF-1, the red-edge I band (0.69~0.73  $\mu$ m), red-edge II band (0.73~0.77  $\mu$ m), a purple band (0.40~0.45  $\mu$ m), and a yellow band (0.59~0.63  $\mu$ m) are newly added bands. GF-6/PMS contains one panchromatic band with 2 m resolution and four multispectral bands with 8 m resolution.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 2. Materials and Methods > 2.2. Data Sources
Type: table
Table: Table 3. Technical parameters of GaoFen-6 (GF-6) (wide field of view (WFV) and panchromatic/multispectral high-resolution (PMS)).

| Payload | Spectral Number | Spectral Type | Spectral Range (nm) | Spatial Resolution (m) | Swath Width (km) | Revisit Interval |  |
|---------|--------------------|---------------|---------------------------|------------------------------|------------------------|---------------------|--|
|         | 1                  | Blue          | 450~520                   |                              |                        |                     |  |
|         | 2                  | Green         | Green 520~590             |                              |                        |                     |  |
|         | 3                  | Red           | 630~690                   |                              | 800                    | Four days           |  |
| WFV     | 4                  | Near infrared | 770~890                   | 17                           |                        |                     |  |
|         | 5                  | Costal        | 400~450                   | 16                           |                        |                     |  |
|         | 6                  | Yellow        | 590~630                   |                              |                        |                     |  |
|         | 7                  | RedEdge1      | 690~730                   |                              |                        |                     |  |
|         | 8                  | Rededge2      | 730~770                   |                              |                        |                     |  |
|         | 1                  | Panchromatic  | 450~900                   | 2                            |                        | _                   |  |
|         | 2                  | Blue          | 450~520                   |                              |                        |                     |  |
| PMS     | 3                  | Green         | 520~590                   | 0                            | 90                     |                     |  |
|         | 4                  | Red           | 630~690                   | 8                            |                        |                     |  |
|         | 5                  | Near infrared | 770~890                   |                              |                        |                     |  |

Payload
Spectral Number
Spectral Type
Spectral Range (nm)
Spatial Resolution (m)
Swath Width (km)
Revisit Interval


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 2. Materials and Methods > 2.2. Data Sources
Type: text

Gaofen-7 (GF-7): The GaoFen-7 (GF-7) satellite, launched on 3 November 2019, carries two laser altimeters with the full waveform for global stereo mapping and is the first submeter-level resolution mapping satellite in the Medium- and Long-Term Development Plan for China's Civil Space Infrastructure (2015–2025) [41–43]. The GF-7 satellite operates in a sun-synchronous orbit with a designed lifespan of 8 years. Equipped with two linearray stereo cameras, it can effectively obtain full-color stereo images with a width of 20 km and a resolution better than 0.8 m and multispectral images with a resolution of 3.2 m. The two-beam laser altimeter on board conducts ground observation with a 3 Hz observation frequency, and the ground footprint diameter is less than 30 m. It acquires full-waveform data with a sampling frequency higher than 1 GHz. The laser altimeter system is designed to improve the elevation accuracy without ground control points of the two line-array stereo mapping cameras [44].

HuanjingJianzai-2 A/B (HJ-2A/B): As the successors of HuanjingJianzai-1 (HJ-1) A/B satellites, HuanjingJianzai-2 A/B (HJ-2A/B) are in a sun-synchronous orbit, with both satellites having the same technical status and a design life of 5 years [45,46]. Each satellite has 4 optical payloads, including a 16 m camera, a hyperspectral imager, an infrared camera, and an atmospheric correction instrument. The 16 m camera payload consists of four visible-light CCD cameras, which can provide a multispectral image with a width of 800 km through field-of-view stitching. The hyperspectral imager has a width of 96 km, while the infrared camera has a width of 720 km. For atmospheric radiation correction, the atmospheric correction instrument can synchronously acquire atmospheric multispectral information with the same field of view as the 16 m camera in orbit.

Beijing-2 (BJ-2): The Beijing-2 satellite (BJ-2) was successfully launched from the Satish Dhawan Space Center in Sriharikota, India, at 0:28 Beijing time on 11 July 2015 (16:28 UTC on 10 July 2015), carried by the PSLV-XL carrier rocket to a 651-km sun-synchronous orbit. The Beijing-2 constellation consists of three 0.8 m resolution optical remote sensing

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satellites [47]. The satellite, manufactured by Surrey Satellite Technology Ltd. (SSTL) in the UK, has a 7-sided structure, weighs about 450 kg, and is about 2.5 m high. The SSTL-300S1 agile platform can provide a fast-swinging ability of 45 degrees and in-orbit realization of 5 imaging modes, including multi-scene, strip, along-track stereo, cross-track stereo, and regional imaging. The VHRI-100 imaging instrument provides in-orbit panoramic and multispectral images with a swath width of about 24 km and a resolution of 0.8 m (ground sampling distance (GSD)) for panchromatic and 3.2 m for blue, green, red, and near-infrared multispectral images.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 2. Materials and Methods > 2.2. Data Sources
Type: text

weighs about 450 kg , and is about 2 . 5 m high . The SSTL - 300S1 agile platform can provide a fast - swinging ability of 45 degrees and in - orbit realization of 5 imaging modes , including multi - scene , strip , along - track stereo , cross - track stereo , and regional imaging . The VHRI - 100 imaging instrument provides in - orbit panoramic and multispectral images with a swath width of about 24 km and a resolution of 0 . 8 m ( ground sampling distance ( GSD ) ) for panchromatic and 3 . 2 m for blue , green , red , and near - infrared multispectral images .

2. Climate data: The land surface temperature data used in the article come from the ERA5-LAND reanalysis dataset provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) [48]. ERA5-LAND uses the simulated land-atmosphere variables from the ECMWF's fifth-generation reanalysis product ERA5 as forcing and is obtained using the modified land surface hydrology model HT-ESSEL and CY45R1. Although it has not undergone data assimilation, the observational data indirectly affect its simulation results. Compared with ERA5, ERA5-LAND has a higher spatial resolution, with a horizontal resolution of up to 0.1° (9 km) and a temporal resolution of 1 h. Due to the limitation of currently available data (https://developers.google.com/earth-engine/datasets/catalog/ECMWF\_ERA5\_LAND\_MONTHLY\_BY\_HOUR#description, accessed on 24 February 2023), the hourly land surface temperature data of ERA5-LAND from 1 January 1981 to 1 December 2022 in the study area was used in the study.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 2. Materials and Methods > 2.3. Methods
Type: text

- 1. Processing of the satellite images and climate data: Preprocessing methods are shared in multispectral data of GF-1, GF-6, HJ-2 A/B, and BJ-2, including five steps: (1) Radiometric calibration; (2) Atmospheric correction; (3) Orthorectification; (4) Image fusion; (5) Image registration. The radiometric calibration and atmospheric correction were performed with the ENVI FLAASH module [44]. After finishing the preprocessing, combining 2 m resolution panchromatic and 8 m resolution multispectral data by the ENVI Gram-Schmidt Pan-Sharpening module to enhance the spatial resolution of GF-1, GF-6, and BJ-2 images [45]. DSM with a 2 m spatial resolution was derived from GF-7 by using the rational polynomial coefficient (RPC) model [41]. Time series temperature data were extracted by Google Earth Engine (GEE) cloud platform (https://earthengine.google.com/, accessed on 24 February 2023).
- 2. Lake area extraction and changes analysis. The visual interpretation method was applied in this study. To improve the interpretation accuracy, the normalized difference water index (NDWI) [47] was introduced to better distinguish water bodies (Figure 2). The NDWI can enhance the identification of water bodies and effectively distinguish water bodies from floating ice. By using the green channel (maximum reflectance of water) and the near-infrared (NIR) channel (minimum reflectance of water), the NDWI was calculated as below:

$$NDWI = \frac{\rho Green - \rho NIR}{\rho Green + \rho NIR}$$
 (1)

Lake area changes, including water and ice area changes, were calculated in ArcGIS 10.8. To characterize the direction of water surface changes, the centroid shift method was used to describe the expansion process of the lake surface (Figures S1 and S2).

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Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 2. Materials and Methods > 2.3. Methods
Type: figure
Figure

Image /page/6/Figure/1 description: A side-by-side comparison of two satellite images, labeled 'a' and 'b', of a glacial lake in a mountainous region, both dated 8 July 2022. Image 'a' shows the natural view of the dark blue lake with floating ice, surrounded by brown mountains and glaciers. Image 'b' overlays a Normalized Difference Water Index (NDWI) map on the same view. A legend for the NDWI shows a color scale from blue (Low: 0.31) to red (High: 0.58). The map displays lower NDWI values in blue over the main body of the lake and higher values in yellow and orange where the glacier meets the water. Both images include a compass rose and a scale bar indicating 1 km.


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 2. Materials and Methods > 2.3. Methods
Type: figure
Figure: Figure 2. Satellite image (a) and image after calculating the NDWI (b).

Figure 2. Satellite image (a) and image after calculating the NDWI (b).


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 2. Materials and Methods > 2.3. Methods
Type: text

3. Uncertainty assessment of glacier lake area. Visual interpretation was employed in extracting the lake area, and errors are unavoidable. Research has shown that mixed pixels caused by spatial resolution are a key factor in error sources. Using an error of one pixel on either side of the defined lake boundary is more appropriate [49]. Therefore, the uncertainty of the individual lake area can be calculated as follows:

$$e = n^{1/2} \times m \tag{2}$$

$$R = \frac{e}{A} \times 100\% \tag{3}$$

where e is the absolute area error (m2) of each glacier lake, n represents the number of pixels on the lake boundary (approximately the ratio of lake circumference to spatial resolution), m is the area of each pixel in the remote sensing product, R is the relative error of a single lake, and R is the area of the lake. The area error obtained from the above equation shows that the total absolute area error of Lake Merzbacher is 0.08 km2, the average relative error is 0.22%, and the relative area error is between 0.2% and 0.68% (Table 4).


Document: Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images
Section: 2. Materials and Methods > 2.3. Methods
Type: table
Table

| Date | Area (km2) | Perimeter (m) | Absolute Area Error | Relative Area Error | Date | Area (km2) | Perimeter (m) | Absolute Area Error | Relative Area Error |
|------|---------------|------------------|------------------------|------------------------|------|---------------|------------------|------------------------|------------------------|
|------|---------------|------------------|------------------------|------------------------|------|---------------|------------------|------------------------|------------------------|

Date
Area (km2)
Perimeter (m)
Absolute Area Error
Relative Area Error
Date
Area (km2)
Perimeter (m)
Absolute Area Error
Relative Area Error