the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
NZ-BeachTopo30: A national-scale and full-coverage 30 m beach topography dataset for New Zealand reconstructed by fusing ICESat-2 and Sentinel-2
Abstract. Beaches provide essential ecological functions and support socio-economic resilience, yet accurate mapping is hindered by systematic limitations in global Digital Elevation Models (DEMs). A critical challenge remains in the intertidal zone, where frequent tidal inundation creates extensive data voids, disrupting the continuity of coastal topography. To bridge this fundamental data gap, we present NZ-BeachTopo30 which is a national-scale and full-coverage 30 m beach topography dataset for New Zealand constructed by fusing ICESat-2 photon-counting altimetry with Sentinel-2 multispectral time series. Using DeltaDTM as a high-precision baseline for the stable backshore, we trained an XGBoost model on ICESat-2 control points and Sentinel-2 spectral-geometric features to reconstruct the missing intertidal topography specifically. SHAP analysis was further employed to interpret the physical driving mechanisms of these predictors. Validation against airborne Lidar confirmed that the dataset accurately recovers elevations in previously void zones with an RMSE of 0.94 m. By integrating these predictions with the DeltaDTM baseline, the final national-scale product achieves robust accuracy with an R² of 0.75 and an RMSE of 1.17 m. This targeted integration significantly expanded valid topographic coverage by 145.8 % from 79.9 km² to 196.5 km². It delivers the first spatially continuous and full-coverage beach topography dataset for New Zealand. This product distinguishes itself by seamlessly bridging the critical intertidal gap that disconnects land and sea in existing global datasets. Given the global availability of ICESat-2 and Sentinel-2, NZ-BeachTopo30 offers a scalable solution for worldwide applications and provides a robust foundation for inundation modeling and coastal management.
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Status: open (until 25 Mar 2026)
- CC1: 'Comment on essd-2025-826', Robbi Bishop-Taylor, 24 Feb 2026 reply
Data sets
New Zealand National-Scale Beach Topography Dataset (30 m): A Fusion of ICESat-2 and Sentinel-2 Yuhao Wang, Hao Xu, Nan Xu, Edward Park, Xuejiao Hou, Jiayi Fang, Zhen Zhang, Yongjing Mao, Huichao Xin, Chunpeng Chen, Yinxia Cao, Yifu Ou, Xinyue Gu, Wenyu Li, Xiaojuan Liu, Conghong Huang, and Qingquan Li https://zenodo.org/records/17785546
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- 1
I thank the authors for a fantastic paper. The remote sensing method is really impressive, and I look forward to inspecting the outputs in more detail. I had a few general comments and areas that I think could be clarified in the paper:
1) The method relies heavily on the use of Sentinel-2 percentile composites to serve as machine learning covariates that reflectance tidal variability. However, due to persistent tidal biases associated with sun-synchronous sensors, satellites like Sentinel-2 rarely observes the same tidal conditions at different locations along the coastline (see Figure 8 in Bishop-Taylor et al. 2018 https://www.sciencedirect.com/science/article/pii/S0272771418308783, and Figure 7, Fitton et al. 2021 https://www.sciencedirect.com/science/article/pii/S2352938521000355). These biases mean that, for example, an 80th percentile composite in one location may observe high tide conditions, while the same 80th percentile composite in another location may only observe mid-tide. How does your approach handle these tide biases, and how do they affect the large-scale consistency of your results? There is currently a single sentence touching on tide variation issues in the manuscript ("First, regional variations in tidal regimes can alter the spectral-elevation relationship, affecting intertidal height retrieval") but I feel it needs to be elaborated on given the importance of these biases for large-scale coastal remote sensing analysis.
2) The paper focuses primarily on open coast beach environments, which is a valuable niche for intertidal elevation modelling that has not seen as much research attention as more sheltered, tide dominated systems. However, I feel it would be valuable to also include either include an example of model performance across more extensive tidal flat environments, or include some discussion points about how/why these intertidal environments were excluded from the study.
3) The data package includes only data for the "NoData" voids in the DeltaDTM dataset. Are there plans to also provide a combined DeltaDTM + NZ-BeachTopo30 data as a single seamless topobathy DEM? Or is the expectation that downstream users will combine these datasets themselves?