The manuscript presents a dataset that extends the temporal understanding of global mining activities. The integration of multiple datasets and the long-term time series analysis (1985-2022) provides significant insights into the dynamics of land disturbance and reclamation. However, there are several critical issues regarding the definition of key concepts, the classification of mine status, visualization, and comparative analysis that need to be addressed to enhance the scientific rigor and clarity of the study. 

1. Clarification and Discussion on "Undefined Mines"

The classification of mining activity status is a core contribution of this study. However, the "Undefined Mines" category requires further revision. Currently, "Undefined Mines" seems to be defined largely by exclusion (i.e., mines that do not fit into "Active" or "Closed" categories based on the MK trend test). This definition is insufficient for a scientific classification system. Authors should provide a positive definition. In addition, the results indicate that 48.9% of the mining polygons are classified as "Undefined." This is a remarkably high proportion, covering nearly half of the dataset. Such a high percentage of "undefined" results undermines the classification model's utility. The authors must discuss why this percentage is so high. 

2. Distinctions between Reclamation, Revegetation, and Greening

The manuscript uses the term "Reclamation" extensively, but the methodology relies on remote sensing indices (BSP and NDVI). There is a conceptual gap that needs to be bridged. The authors should explicitly differentiate between "Greening" (increase in vegetation index), "Revegetation" (establishment of plant cover), and "Reclamation" (restoration of ecosystem function and land capability). 

3. The x-axis labels in Figure 6(a) (Year) are currently stacked and overlapping, making them unreadable.

4. The manuscript lacks comparison with more recent and regionally focused high-resolution products. The authors should compare their findings with the recent study on tropical mining expansion: Sepin, P., Vashold, L. & Kuschnig, N. Mapping mining areas in the tropics from 2016 to 2024. Nat Sustain 8, 1400–1407 (2025). https://doi.org/10.1038/s41893-025-01668-9

Comment on essd-2025-583

This manuscript presents a valuable global dataset on mining disturbance and reclamation for the period 1985–2022, generated by integrating multiple mining inventories with remote sensing–based trend indicators. The dataset has clear potential for broad applications in land-use and disturbance monitoring. However, the current version does not yet fully demonstrate the superiority, accuracy, or methodological rigor of the merged product relative to its source datasets. I therefore recommend major revision to strengthen validation, clarify key methodological choices, and provide additional case-based evidence supporting the dataset’s claimed advantages.

Major Comments

1. Temporal analysis: Since the innovation of this study lies primarily in its long-term time series analysis,the temporal validity, indicator selection, and underlying assumptions need clearer explanation. The following issues should be addressed:

• Is the start time of each mine explicitly considered? Please clarify the baseline year used to define mining-induced disturbance for each mining polygon.
• Do all mines—regardless of when they became active—use the same analysis window (2018–2023) for trend assessment? If so, this assumption requires justification. Besides, What is the rationale for selecting the 2018–2023 period for determining mining activity status? A stronger explanation is needed to demonstrate that this short period is appropriate and representative for diverse mining types globally.
• Does the method account for the fact that mining boundaries expand or contract over time? Assuming static boundaries may significantly affect temporal interpretation, and this assumption should be explicitly stated and evaluated.
• NDVI saturation may lead to underestimation of recovery trends in medium-to-high biomass areas. What would change if alternative indicators—such as NIRv—were used? A sensitivity discussion or justification would strengthen confidence in the results.
• As shown in the result, nearly half of the mining polygons are classified as “undefined”. This proportion is unexpectedly large and suggests limitations in indicator sensitivity or threshold selection. Further explanation or sensitivity testing is needed to ensure the robustness of the classification framework.

2. Comparisons and calidations: The dataset is derived from merging two existing mining inventories, but the manuscript does not convincingly demonstrate accuracy improvements. Although the merged dataset is more complete in area and count (as shown in Fig.2 and Table 1), the manuscript does not quantitatively demonstrate that boundary accuracy or delineation precision is improved relative to the individual input datasets. To support claims of data enhancement, the authors should consider conducting cross-dataset quantitative comparisons using the same validation samples or adding case-based evaluations to demonstrate where and why the new dataset performs better.

Minor Comments

1. Line 306-312：These sentences should be moved to the relevant figure caption. Please check for similar cases throughout the manuscript.
2. Figure 3(a): The lower chart duplicates information already shown in Figure 3(b). Please remove it to reduce visual clutter.
3. Figure 3(b), upper plot: The purpose for scaling the y-axis by 1,000 is unclear and seems unnecessary.
4. Figure 3(b), lower images: The images are too blurry. Please regenerate them using downloaded satellite imagery rather than screenshots from Google Earth.
5. Percentages in Sections 3.2 and 3.3 are inconsistently formatted (some integers, some with one decimal place). Please harmonize formatting across the manuscript.
6. Please provide a dataset user guide / metadata documentation, explaining the meaning, units, and calculation logic of each field in the dataset. This will significantly improve usability.