Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation
Authors: Jan Pauls
Max Zimmer
Berkant Turan
Sassan Saatchi
Philippe CIAIS
Sebastian Pokutta
Fabian Gieseke
Venue: ICML 2025 poster (2025)
Keywords: canopy height
remote sensing
satellite
deep learning application
sentinel
gedi
Primary area: applications->chemistry_physics_and_earth_sciences
TL;DR: We estimate tree canopy height over time using Sentinel Satellite Time Series Data and a custom Deep Learning approach.

Abstract: With the rise in global greenhouse gas emissions, accurate large-scale tree canopy height maps are essential for understanding forest structure, estimating above-ground biomass, and monitoring ecological disruptions. To this end, we present a novel approach to generate large-scale, high-resolution canopy height maps over time. Our model accurately predicts canopy height over multiple years given Sentinel 2 time series satellite data. Using GEDI LiDAR data as the ground truth for training the model, we present the first 10 m resolution temporal canopy height map of the European continent for the period 2019–2022. As part of this product, we also offer a detailed canopy height map for 2020, providing more precise estimates than previous studies. Our pipeline and the resulting temporal height map are publicly available, enabling comprehensive large-scale monitoring of forests and, hence, facilitating future research and ecological analyses. For an interactive viewer, see https://europetreemap.projects.earthengine.app/view/europeheight.

Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation
Jan Pauls 1* Max Zimmer Berkant Turan
Sassan S Philippe Ciais*
Sebastian Pokutta" Fabian Gieseke
Abstract
Actial Scaming (ALS)
With the rise in global greenhouse gas emissions, accurate large-scale tree canopy height maps are essential for understanding forest structure, esti- mating above-ground biomass, and monitoring ecological disruptions. To this end, we present novel approach to generate large-scale, high- resolution canopy height maps over time. Our model accurately predicts canopy height over mul- tiple years given Sentinel-1 composite and Sen- tinel 2 time series satellite data. Using GEDI LiDAR data as the ground truth for training the model, we present the first 10 m resolution tempo- ral canopy height map of the European continent for the period 2019-2022. As part of this prod- uet, also offer detailed canopy height map for 2020, providing more precise estimates than previous studies. Our pipeline and the resulting temporal height map are publicly available, en- abling comprchensive large-scale monitoring of forests and, hence, facilitating future research and ecological analyses.
Pauls Lang al.
Turuhanova
Figure I. Comparison of six canopy height maps with precise mea- surements obtained via acrial laser scanning (ALS). The patches contain tall trees exceeding 30 m in height. Our model is the only one that can accurately estimate the height of such trees.
of numerous methods, this quantification remains insuffi- cient for detailed and effective policymaking (Cook-Patte Pattonn et al., 2020; Cuni-Sanchez et al., 2021). An essential part of this quantification is the monitoring of forest ecosystems, in turn allowing their management and conservation. To support climate adaptation and mitigation strategies, accu- rate and up-to-date information on forest health and carbon balance critical to evaluate the current state of forests, to implement measures to prevent forest loss, and to improve management strategies (Friedlingstein et al., 2019).
A key approach to assess forest conditions is based mea- suring estimating tree heights, which results in height values that are used to approximate wood volume, so-called above-ground biomass and, consequently, the carbon stored in trees (Schwartz et al., 2023). A traditional way to ob- tain corresponding measurements is to manually measure individual trees, resulting in so-called National Forest In- ventories (NFT). Such inventories monitoring, but are very costly and also lack global reach. This issue is worsened by varying forest monitoring efforts and techniques across different nations with different finan- cial resources (Sloan & Sayer, 2015). However, advances in Earth observation and machine learning now enable auto- mated, comprehensive global forest assessments by leverag- ing satellite data, including optical, radar, and LiDAR mea- surements (Hu et al., 2020). The resulting high-resolution canopy height maps are essential for understanding forest dynamics and supporting climate change mitigation efforts.
1. Introduction
As global carbon emissions continue to rise, meeting the goals of the Paris Agreement' requires a comprehensive un- derstanding of all climate-related factors. This includes pre- cise quantification and temporal monitoring of carbon sinks. However, despite decades of research and the development
essential for forest
"Equal contribution 'Department of Information Systems, Uni- Society,
versity of Münster, Germany DDpaatment for Science, and Technology, Zuse Institute Berlin, Germany 'Jet Propulsion Laboratory (JPL), California Institute of Technology, USA 'Laboratoire des Sciences du Climat et de l'Eniironeement, LSCE/PPSL, France 'Department of Computer Science, Univer- sity of Copenhagen, Denmark. Correspondence to: Jan Pauls sjan,pauls uni muenster.de>.
Proceedings of the 4nnd International Conference on Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).
httpp:: /atpss///ufccc.o// ittpppocess---ieeooooooo and-meetiin / the-pari aarrrissgggeeen agreement
Recent studies have advanced canopy height prediction using classical machine learning methods (Potapov et al.,

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Capturing Temporal Dynamics in Large-Scale Tree Canopy Height Estimation
2021; Kacic al., 2023; Loo & Wang. 2024) and deep learn- ing approaches (Schwartz al., 2024; Fayad et al., 2023; Lang et al., 2023; Pauls et al., 2024; Liu et al., 2023; Tolan et al., 2024; Fayad et al., 2025), with the satellite-dependent resolution varying between 30 m (Landdst), 10 m (Sentinel- 1, -2), 3 m (PlanetLabs) and 60 cm (Maxar) with only the Sentinel and Landsat program being openly available. How- ever, almost all studies focus on predicting canopy height for single year, despite that being insufficient (Bevacqua et al., 2024). Temporal dynamics of forests are essential for identifying carbon sources and sinks and assessing for- est responses to natural or human-induced stressors such as drought, diseases, or environmental changes. Although some methods have explored temporal tree height mapping* at local scales (Kacic et al., 2023) and Turubanova et al. (2023) produced a map for Europe, no approach has yet tackled this challenge scale combined, which is vital for assessing forest details.
2. Background
Tree canopy height estimation has advanced significantly through integrating satellite data from Sentinel (European Space Agency, 2024), Landsat (Williams et al., 2006). GEDI (Dubayah al., 2020), ICESat (Abdalati et al., 2010), and Aerial Laser Scanning (ALS). The goal is to predict the tree height for each pixel in satellite images. However, ground truth data like GEDI measurements are limited and sparsely distributed across the globe. This section covers the fundamental challenges of creating such maps.
2.1. Satellite Imagery
Openly available satellite imagery for tree canopy height prediction is primarily sourced from three key missions: Landsat, Sentinel-1, and Sentinel-2. The Landsat pro- gram (Williams et al., 2006), run by NASA since 1972, offers optical multi-spectral imagery with a 30 m resolution and a 16-day revisit cycle, meaning that new data are col- lected for any region every 16 days. The Sentinel missions, operated by the European Space Agency (ESA) since 2014, 4, include Sentinel-1 with Synthetic Aperture Radar (SAR) and Sentinel-2 with multispectral sensors. Both provide 10 m resolution images and a global revisit time of about 5 days, allowing more frequent forest monitoring. While airborne data can achieve higher resolutions of up to 10 cm, it is often limited to specific regions and not widely acces- sible, making spaceborne imagery the preferred choice for large-scale height mapping.
resolution of 10m and greater
In this work, we introduce an approach for generating large- scale, temporal tree height maps. Specifically, we use a cus- tom deep learning approach that predicts tree canopy height from Sentinel image data, using the sparsely-distribute space-borne GEDI LiDAR data as a ground truth.
Contributions. We address the task of estimating tree canopy heights given satellite time series data as input. The main contributions made in this work are follows:
1. We provide a model capable of accurately tracking for- est height changes 10 m resolution across the entire European continent, enabling consistent detection of growth and decline from 2019-2022.
While these satellite missions provide global-scale image data, single images are often not suitable for canopy height prediction. Radar satellites like Sentinel-1 can suffer from rain interference and noise, while multispectral sensors like Sentinel-2 and Landsat struggle with cloud and cirrus pene- tration. These images require preprocessing, such as cloud removal, color correction, and atmospheric correction, to convert from tat-ooffte-Attssphhee (TOA) to Bottom-of- the-Atmosphere (BOA) images.
2. We present a canopy height map of Europe for the year 2020, providing more accurate measurements and finer spatial details than previous studies, both in quantita- tive and qualitative assessments.
3. We demonstrate that using 12-month time series of Sentinel-2 imagery, rather than using a single aggre- gated composite, yields substantial performance gains.
To further address these issues, temporal composites are used to aggregate images over time, employing tech- niques like per-pixel median calculation (Pauls et al., 2024; Schwartz et al., 2024; Fayad et al., 2023) or the Best- Available- Pixel approach (Senf & Seidl, 2021). These meth- ods mitigate adverse weather and atmospheric effects, en- suring more consistent and reliable images for tree canopy height analysis. However, temporal aggregation often re- sults in significant information loss, discarding valuable seasonal variations such as leaf-on vs. leaf-off seasons. This hinders capturing crucial temporal dynamics, but has thus far only been tackled for crop yield prediction and classi- fication (Fan et al., 2021; Johnson & Mueller, 2021) and general satellite image processing (Tarasiou et al., 2023).
The entire pipeline and model weights are publicly released on GitHub² and the resulting tree canopy height maps are accessible through Google's 's Earth Engine* (Gorelick et al., 2017), ensuring reproducibility and facilitating research on large-scale forest monitoring, forest structure analysis and above-ground biomass estimation.
"We define temporal maps as those which provide canopy heights over multiple years, independent of the generation process. /https:://ithuub..co// wtt.:///////////////oooo Turopee- "httess//errgeerremmppppre
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Capturing Temporal Dynamics in Large-Scale Tree Canopy Height Estimation
Additionally, aggregation removes the spatial shifts caused by geolocation inaccuracies, thus losing the opportunity to leverage this variability for more accurate predictions. Precisely. Sentinel-2 imagery exhibits mean geolocation offset of about 4 m (Yan et al., 2018), causing each pixel's reflectance to be influenced by its surroundings. Leveraging this offset across multiple images can enhance the model's ability to detect structural details, such as distinguishing a large tree's 's borders within a forest of smaller trees. This results in more precise height predictions compared to using aggregated images. Wolters et al. (2023) demonstrated that using multiple Sentinel-2 images outperforms single-image methods by leveraging temporal information and geoloca- tion shifts. Our work further builds upon this approach.
3. Approach
Our methodology integrates multi-source satellite imagery and GEDI LiDAR data to produce high-resolution, temporal canopy height maps for Europe. Next, we detail the data sources, preprocessing steps, and the design choices made.
3.1. Data
To construct our dataset, we integrate information from three key sources: Sentinel-1, Sentinel-2, and GEDI. Each dataset undergoes rigorous preprocessing to ensure compatibility and optimal quality. To keep the best projection accuracy, we use a tiling system following the Universal Transverse Mercator (UTM)* system and process the data in these tiles.
2.2. Tree Canopy Height Estimation
Sentinel-1 and Sentinel-2. Sentinel-1 provides Synthetic Aperture Radar (SAR) measurements in two polarizations: VV (vertical-vertical) and VH pertical-horizontaa), ac- quired in the Interferometric Wide Swath (IW) mode. Im- ages are collected from both ascending and descending or- bits, resulting in four distinct channels per tile. Due to the high noise levels inherent in SAR data, we aggregate the measurements over temporal space by computing the per- pixel median across all acquisitions within a year, thereby mitigating noise and enhancing temporal consistency. The data is collected in the Sentinel UTM coordinate system, where each tile spans 100 km x 100 km.
Tree canopy height mapping has evolved from classical machine learning (Kacic et al., 2023; Potapov et al., 2021) to advanced methods such as convolutional networks (Liu et al., 2023; Yan et al., 2018; Lang al., 2023) and vision transformers (Fayad et al., 2023; Tolan et al., 2024).
A key challenge in training models for temporal canopy height estimation is obtaining accurate ground-truth data. This data can come from national forest inventories or Li- DAR measurements, either airborne or spaceborne. Air- borne LiDAR provides high-resolution data but is limited to local areas. In contrast, NASA's GEDI mission offers broader geographic and temporal coverage with spaceborne LiDAR, though at coarser spatial resolution and with oc- casional geolocation inaccuracies, GEDI measures canopy heights using laser shots with a 25 m footprint, but only about 4% of Earth's ' surface is covered during the satellite's s operational period, and spatial alignment across different years is virtually non-existent. This inconsistency compli- cates the use of GEDI as reliable temporal training dataset.
Sentinel-2 multispectral imagery is a key part of our ap- proach. We use the Level-2A surface reflectance product (BOA) available via the Copernicus AWS, selecting sin- gle best image each month based on minimal cloud cover, thereby ensuring minimal contamination and consistent data quality. This yields a temporal sequence of 12 monthly images per tile and year (one per month), thereby preserv- ing seasonal patterns important for vegetation monitoring. We include all Sentinel-2 bands except B10 0 (cirrus). Each band's reflectance values are normalized by fixed factor (ef. Table 1), mapping values to [0, 1]. Rather than employing conventional min-max zero-mean normalization tech- niques, which are substantially affected by the presence of clouds and cirrus, we identified value ranges for each band that contain "valuable" information, ensuring data quality is maintained for analysis. By retaining monthly variability instead of aggregating temporal data, our approach captures seasonal dynamics, such as transitions between leaf-on and leaf-off states, which are crucial for vegetation monitoring.
In consequence, most studies focus on estimating height for a single year, with few addressing multi-year time series (Dixon et al., 2025; Kacic et al., 2023; Turubanova et al., 2023). Mapping efforts over multiple years or seasons face significant computational and storage challenges and can result in fluctuating height estimates due to varying satellite conditions and model uncertainties. A common approach is to just apply single-year model to data from other years, but this fails to capture temporal patterns. Consequently, post-processing techniques like moving averages, trend de- tection, and cut-detection are used to smooth out year-to- year variations. Developing a robust, large-scale framework for temporal tree canopy height mapping remains one of the key research challenges in forest monitoring.
GEDI. GEDI LiDAR data provides sparse height mea- surements that are essential for model supervision. We use
"The UTM coordinate system divides the Earth into 120 zones, each 6" of longitude wide, using transverse Mercator projections to minimize distortion. Each zone is further divided into tiles following the Sentinel-2 tiling system for better data handling.
AAlhhough GEDI not only measures trees, this can be neglected here, as forest masks are used for filtering before further analysis.
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Capturing Temporal Dynamics in Large-Scale Tree Canopy Height Estimation
predict canopy heights across spatial and temporal scales using modified Huber loss (Pauls et al., 2024).
Table 1. Grouped band normalization for Sentinel-2.
Bands
Divisor
B1 (Coastal)
0.9 x 10a
Modified Huber Loss. Our final loss function is the Hu- ber loss, as effectively balances penalizing small errors while being less sensitive to outliers compared to 1.2 loss. This is especially important because GEDI geolocation data often contains outliers. Since GEDI's geolocation is not per- fectly precise, we incorporate a shift mechanism (Pauls et al., 2024), to account for systematic geolocation offsets. This mechanism allows the model to adjust an entire flight path (referred to as a "track") by a predefined maximum offset, such as 10 m, to reduce errors caused by consistent mis- alignment. Additionally, because the labels in our dataset are sparse meaning that many pixels lack corresponding label values- compute the loss only for labeled pixels and ignore unlabeled pixels during training.
B2, B3, B4 (Visible), B5
1.8 x 103
B6, B7 (Red Edge), B11, B12 (SWIR)
3.6 x 10a
B8, B8A (NIR), B9 (Water Vapor)
5.4 x 10
the Level-2A product. transforming its geolocations to align with the Sentinel UTM grid. Several filters are applied to ensure data quality: valid quality flags, non-degraded shots, and a sensitivity threshold of 0.9. We consider only high- power beams (IDs 5-8) due to their superior signal-to-non toisee ratio. Height values are constrained to a plausible range of [0, 100 m| to exclude outliers. We utilize the rh-98 metric (relative height 98%), which corresponds the height be- low which 98% of returned photons are recorded, providing a robust estimate of canopy height. We use data from the years 2019-2022, where we have full coverage.
Optimization Setup. We use Adam (Kingma, 2014) with an initial learning rate of 0.001, weight decay of 0.01, and gradient clipping at 1.0. We follow best practices (Li et al., 2020; Zimmer et al., 2023) and use linear learning rate scheduler with a 10% warmup, training for 400,000 itera- tions with a batch size of 16 (corresponding to 8 epochs).
3.2. Model Architecture
We adopt three dimensionaiii (3D) U-Net inspired by Çiçek et al. (2016), originally proposed for volumetric segmen- tation tasks, and modify it to output single-channel tree canopy height for each input pixel. Unlike prior approaches relying on static images or single composites (Schwartz et al., 2024; Pauls et al., 2024), our model processes stack of 12 monthly Sentinel-2 images concatenated with an ag- gregated Sentinel-1 composite. This approach allows the model to use potential seasonal vegetation changes, and possibly exploit geolocation offset in Sentinel-2 imagery by incorporating information from neighboring pixels.
Dataset Size. Our dataset comprises 800,000 randomly selected patches, each measuring 2.56 km x 2.56 km (ap- prox. 0.15% pixels have labels per patch), totaling 8 TTB in size. To minimize computational load and data transfer, we use 10% subset for training and hyperparameter tuning for the different baselines, as outlined in Section 4.1. The final model is then trained on the entire dataset.
Post-Processing A problem of temporal height maps is fluctuating predictions due to uncertainties in the data, such as inconsistent color calibration and varying cloud cover. To mitigate this, we apply a quadratic smoothing spline to capture the underlying trend while reducing noise. We set the smoothing parameter to 5 balance fidelity to the data with improved temporal consistency, ensuring a clear and more interpretable prediction visualization.
The architecture follows the standard encoder-devoder U- Net structure, employing 3D convolutions throughout to capture both spatial and temporal dependencies. We adapt the final output layer to generate a single-channel height map rather than multi-class segmentation mask. In parallel, Sentinel-1 data aggregated into a single median-basee image over the year- is repeated across these time slices so that the model can exploit both radar and optical signals. The encoder path slowly reduces the temporal dimension from its initial size down to 1 at the bottleneck, and each skip connection likewise applies a 3D convolution with kernel size matching the remaining temporal images, effectively collapsing the temporal dimension to a single slice.
4. Results
This section presents the results of our temporal tree canopy height model. We focus on the accuracy of our predicted tree canopy height maps, comparisons with existing models, and generalization across european forest regions.
3.3. Model Training
All results in this section are based on 1,500 randomly se- lected validation points, see Figure 2 for the distribution. At each validation point, we select 2.56 km x 2.56 km area to collect all available GEDI labels that match the same criteria as in Section 3.1. We compare with five existing canopy
Our training strategy is designed to address the challenges of sparse ground truth data, geolocation inaccuracies, and the need for temporally consistent predictions. The final model, based on the 3D U-Net architecture, is trained to
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Capturing Temporal Dynamics in Large-Scale Tree Canopy Height Estimation
Table 2. Comparison of various model configurations for 2020.
The 'STACK" models outperform the 'COMPOSITE' models, and integrating 3D features further boosts performance. Overall, the 3D-STACK-MULNN MAAACK-MLTTYEEAA YAR model achieves the best results.
MAE [m]
MSE [m²]
AD-COMPOS/TT-2222)
5.66
85.93
2D- COMPOSITE-MULTTYERE
5,43
82.70
ADD-STACK-005
5.51
81.31
5.17
78.07
Figure Spatial distribution of 1,500 randomly selected valida- tion locations across Europe. Each location covers 2.56 km x 2.56 km area which estimated canopy heights are evaluated against GEDI measurements. Note that duc GEDI's flight path, no labels are available above 5..6NN.
AD-STACK-2022 DO-STACK-MULTTYENN
5.48
80.32
5.05
77.40
After training on the 2020 dataset, we evaluate the 2D- STACK-2022 JD-COMPOSITE-2020 and 3D-STACK- 2020 models on 2019-2022 satellite imagery to assess their generalization without multi-year training.
height maps (Liu et al., 2023; Tolan et al., 2024; Lang et al.,
2023; Turubanova et al., 2023; Pauls et al., 2024), down- loaded from Google Earth Engine, and resample all maps to 10 m a global projection system (EPSG:4326).
C2: Single- Year Models for Each Year. This configu- ration trains independent models for each target year. We develop three variants per year-2D-STACK-YEA. COMPOSITE- YEAR, and 3D-STACK- YEAR with each model trained and validated solely on data from its respec- tive year. This approach evaluates how well specialized, single-year models capture inter-annual variability.
4.1. Establishing Baselines
2D-
We evaluate our approach by comparing various model con- figurations. None of these models use explicit time-stamp information (e-g., separate channel for month or year). Instead, they differ in their treatment of satellite data (e.g., full monthly stacks median composites) and the span of training data (single-year vs. multi-year), In all configura- tions, Sentinel-1 data aggregated into median composite concatenated along the channel dimension.
C3: Multi-Year Models. To enhance generalizabil- ity across years, train models on multi-year data (2019-2022). We use both stack-based (2D-STACK- MULTIYEAR, 3D-STACK DD-STACK-MULTTYEAR and composite- based (2D-COMPOSITE- MUN.TI approaches. While the 2D-STACK architectures process multi-month data by concatenating all months into single input dimension, they do not explicitly exploit temporal relationships.
C1: Single-Year Models Applied to Multiple Years. In this configuration, model is trained on data from a single reference year and used to predict canopy height in other years without any retraining. We focus on 2020 due to the availability of published models for this period (Lang al., 2023; Tolan et al., 2024; Turubanova et al., 2023; Pauls et al., 2024), enabling direct comparisons. We explore the following three variants of this approach:
4.2. Quantitative Evaluation
We begin by comparing our different model configura- tions for the reference year 2020 across two different met- rics: Mean Absolute Error (MAE) and Mean Squared Error (MSE). As the resulting map is targeted towards forest assessment, we compare it only against labels ex- ceeding 7 m, to exclude labels over acres and grassland. Table excludes ID-COMPOSITE-- YEAR, 2D-STACK- Year, and 3D-STACK- YEAR to avoid redundancy, as 2D- COMPOSITE- YEAR the same as DD-COMPOSITE-2020 for the 2020 comparison.
2D-STACK-2020 A 2D U-Net trained the full 12-month Sentinel-2 stack of 2020, where monthly channels are flattened into single tensor of shape [B, H. W, 12 x Channels].
2D- DD-COMPOSITE-2000 2020: A 2D U-Net trained on the median composite of the 12 monthly images, reduc- ing each year's input to a single image of shape [B, H, W, Channels).
Another key comparison is how the metrics vary across different years. Table 3 presents the MAE for each year for all model configurations, as well as the average MAE over all years. The results demonstrate that 3D-STACK- MULTiYFAR significantly outperforms all other configu-
3D-STACK-2020: A 3D U-Net (similar to our model) trained solely on the 2020 dataset, using the 12-months stack as a spatio-temporal volume B, H, W, 12, Channels].
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Capturing Temporal Dynamics in Large-Scale Tree Canopy Height Estimation
Table MAE comparison over multiple years for all model configurations. The achieves the lowest MAE. Within each variant (2D- COMPOSITE, 2D-STACK, 3D-STACK), multi-year training substantially outperforms single-year training. Notably, best single-year model ADD-STACK-20222 only matches the performance of the weakest multi-ycar model -2D-COMPOSITE-MUU MULTIYEAR).
Table 4. Comparison of performance metrics for different models in 2020. Despite the coarser 10m resolution of Sentinel- 1/2 (S1/2) compared to Planet (3 m) and Maxar (60 cm), our model yields highly accurate maps and achieves best overall performance.
model consistently
Source
MAE [m]
MSE [m²]
I|a
Tolan (2024)
Maxar
11.25
212.14
0.409
Liu al. (2023)
Planet
8.17
138.25
0.481
2019
2020
2021
2022
Avg.
Lang el al. (2023)
5.74
84.68
0.488
Pauls al. (2024)
S1/2
5.46
83.14
0.536
AD-COMPOSITE-2DE
6.53
5.66
5.97
5.93
6.02
Turubanova et al. (2023)
Landsat
12.39
252.57
0.318
DDD-COMPOSITEYYAE YEAR
6.08
5.66
5.83
5.64
5.80
COMPOSITE-MULTVR
5.76
5.43
5.36
5.39
5.48
Ours
S1/2
4.76
74.28
0.591
AD-STACK-2022
5.65
5.51
5.38
5.48
5.50
ED-STACK-YEAL YEAR
5.31
5,51
5.28
5.12
5.31
SES-STCCK-MULTYY
5.18
5.17
4.93
4.90
5,04
Table S. Direct MAE comparison across different years between our model and Turubanova al. (2023). On average, model improves predictions by 61 %6 with notable year-to-year variability (reflected standard deviation of 0.27 m).
5.55
5.48
5.25
5.34
5.41
3D-STACK- STAAACCK-YYRR
5.32
5.48
5.23
4.96
5.25
3D-STACK-MDL STUUCKK-MUU
5.09
5.05
4.84
4.83
4.95
Distribution of Errors for Different Tree Heights
20
2019
2020
2021
2022
Avg. (all years)
Mean Esror (m)
Turubanova el al.
12.38
12.39
11.37
12.05 (-)
Ours
4.77
4.76
4.53
4.48
4.69 (4.64)
-20
European map at 30 m resolution (Turubanova et al., 2023).
-40
Despite the coarser resolution of Sentinel-122 (10 m) com- pared to Planet (3 m) or Maxar c60cm), models utilizing Sentinel data achieve higher accuracy, likely due the avail- ability of near- infrrrd and shortwave-infrene shown in Table 4, our model, AID-STACK-MURT MULLTIYEAR-L. achieves the best performance across all metrics, with an MAE of 4.76 m-representing a 13% improvement over the next-best model (Pauls et al., 2024). Similar improvements are observed for MSE and R².
10: 15m
15.20m
25m
25 30m
30-35m
35. 40m
GEDI Label Bins
Liu al.
Turubanova al.
Lang al.
Tolan al.
Pauls et al.
Ours
bands. As
Figure 3. Boxplots for each model showing the 2020 mean error in
every 5 m bin between m and 40 m. Although Liu et al. (2023), Pauls al. (2024) and Lang et al. (2023) perform well on smaller trees, our models performs especially well for taller trees.
Turubanova al. (2023) offers the only other high- resolution, multi-year tree canopy height map trained at European scale, with predictions spanning 2001-2021. Ta- ble 5 compares the MAE of their map for 2019-200 with the performance of our model, AD-STACC-MMLLTTYEAAAA evaluated from 2019-2022. Our model consistently out- performs Turubanova et al. (2023) across all shared years, achieving MAE values less than half theirs (61% improve- ment). Although both models exhibit notable inter-annual variations, likely due to factors such lighting conditions, forest dynamics, and differences in label distributions, the results demonstrate the superior accuracy and robustness of our model for European tree canopy height estimation.
rations, both in single-year and multi-year analyses. In contrast, 2D-COMPOSITE consistently shows the poorest performance, followed by 2D-STACE and 3D-STACK.
4.3. Method Comparison
As outlined in Section 3.3, all models in Table 2 and Table 3 were trained on smaller dataset. After identifying the best performing configuration, we trained a new model, the full dataset, which we will refer to as Ours from this point forward. Unless stated otherwise, all values in this section are based on labels greater than 7 m to focus on forest-related data.
Tree canopy height maps often suffer from reduced accuracy as tree height increases; a serious problem as tree canopy height maps often used for above-grount biomass pre- diction, where biomass typically scales non-linearly with height following an allometric power-law relationship. Fig- ure shows the mean error within 5 m bins between 10 m and 40 m, with a negative mean error denoting that the pre- diction on average lower than the label. For smaller trees
Quantitative Evaluation. Table 4 reports MAE, MSE, and coefficient of determination R²2 for five maps: global map at m resolution (Tolan et al., 2024), a European map m resolution (Liu et al., 2023)?, global maps at 10 m resolution (Lang et al., 2023; Pauls et al., 2024), and
Liu al. (2023)'s 2019 map is included completeness.
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Capturing Temporal Dynamics in Large-Scale Tree Canopy Height Estimation
Google Maps
Ours
Lang et al.
Liu et al.
Tolan et
Pauls et
Turubanova et al.
35
Figure Qualitative comparison of canopy height maps for the reference year 2020: Liu al., Tolan al., Lang al., Pauls al., Turubanova et al. (2020) and our model, ED-STACK-MULTT YEARL.
ranging from 10 m to 20 m, Liu et al. (2023); Pauls et al. (2024); Lang et al. (2023) and our model perform reason- ably well, whereas Tolan et al. (2024) and Turubanova et al. (2023) show mean errors around 10 m, which worsen further for taller trees. Notably, from 20 m onward, our model outperforms all others, achieving mean error of approximately -5 m between 35 and 40m, significantly reducing the error compared to the next best model by Lang et al., particularly for these ecologically critical tall trees. This substantial improvement highlights our model's ability to provide more reliable tree canopy height estimates for tall trees, which are key contributors carbon storage.
Qualitative Evaluation. While quantitative evaluation is essential, visual quality is equally important for tree canopy height prediction. Figure 4 visually compares Liu et al. (2023), Tolan et al. (2024), Lang et (2023), Pauls et al. (2024), Turubanova et al. (2023) and our model. The first column shows high-resolution satellite image from Google Maps (not necessarily from 2020) whereas all other columns show the height between Om (black) and 35 m (light yel- low). Despite its relatively modest metrics, the high reso- lution of Tolan et al. (2024)'s map makes effective for canopy detection. Liu et al. (2023) identifies trees with higher resolution and does not suffer as much from satu- ration effects Tolan al. (2024) and Turubanova al. (2023). While model lacks the high resolution of Liu et al. (2023) and Tolan et al. (2024), it surpasses Pauls et al. (2024), Lang et al. (2023), and Turubanova et al. (2023). Unlike other maps that assign similar heights to all forest patches, our map is the only one diffcrentiating various forest heights in smaller patches (bottom of first row).
Figure 5 shows scatterplot for each model along with its H² value, where Rt2 (coefficient of determination) quan- tifies the proportion of variance in the tree canopy height labels explained by the model predictions (a 1 corresponds to a perfect fit, a 0 means that no variance is explained). Here, Rt2 is calculated using all labels and I8 takes only la- bels exceeding m. The plots reveal that Tolan et (2024)
and Turubanova et al. (2023) struggle with taller trees, neither predict heights beyond 25 m. Lang al. (2023),
One strong improvement of model is the detection of forest patches with very high trees. Figure shows small, but representative example of forest with patches of high trees, where we have very high-quality ALS data. Although Liu et al. (2023) and Pauls et al. (2024) detect that these trees are at a different height than their surrounding, they fail correctly estimate the height. Our model able detect those patches and accurately predict its height.
Liu et al. (2023), and Pauls et al. (2024) tend to saturate at 35 m. In contrast, our model exhibits the narrowest point cloud, with the highest density areas aligning closely with perfect fit line, and achieves strong performance up to 40 m-45 m. Consequently, we improve R² from 0.793 to 0.819, and for labels over 7 (R2), from 0.536 to 0.591.

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Capturing Temporal Dynamics in Large-Scale Tree Canopy Height Estimation
Ours
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Figure Left: Scatterplots between 2020 GEDI labels and prediction for Lang et al.: Liu et al.; Pauls et al.: Tolan et al.; Turubanova and our model including R² for all labels and H& for labels exceeding 7 m. Right: Histograms of GEDI labels and all maps. Turubanova al. and Tolan et al. saturate 28 m, our model is the only one matching above 40 m.
Google Maps
0020-2011 2021 2019 2022-2019
Sentinel-2 imagery time series, our approach avoids the in- formation loss of median composites and captures essential seasonal variations and geolocation shifts. Trained on GEDI LiDAR data, our model produces a highly accurate 10 m resolution tree canopy height map of Europe for the years 2019 to 2022. Our model outperforms existing state-of-the- art models, achieving significantly lower errors. Notably, it drastically improves accuracy for tall trees, essential for precise biomass estimation and carbon stock assessments. Our temporal maps capture forest dynamics, such as defor- estation and growth patterns, offering valuable insights for forest monitoring and ecological analyses. We believe that the publicly released pipeline and tree canopy height maps will support further research and inform decision- makinng in forest management and conservation.
--10
(-10, -S]
0 (-5, $]
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Figure 6. Temporal maps illustrate the expansion of deforestation from 2019 to 2022. This is observed by comparing differences be- tween each year, visible in both solitary forest patches surrounded by open land and within densely forested arcas.
A Acknowledgements
As our model is applied to 2019-2022, we can observe tem- poral dynamies such as deforestation, identifying where trees have been cut down. Figure 6 shows observable defor- estation in two areas. However, mapping minor tree growth is challenging due to the short time frame and slow natural growth rate of trees. In contrast, in forest plantations where fast-growing tree species are actively managed, significant growth can be observed more easily because the acceleration in growth outweighs the uncertainty in our predictions.
This work was supported via the A14Forest project, which is funded by the German Federal Ministry of Education and Research (BMBF; grant number 011S23025A) and the French National Research Agency (ANR). It was also par- tially supported by the Deutsche Forschunngsemmeinsccafff (DFG, German Research Foundation) under Germany's 's Ex- cellence Strategy The Berlin Mathematics Research Cen- ter MATH+ (EXC-2046/1, project ID: 390685689). We also acknowledge the computational resources provided by the PALMA II cluster at the University of Münster (subsidized by the DFG; INST 21 1/667-1) as well as by the Zuse Insti- tute Berlin. We also appreciate the hardware donation of an A100 Tensor Core GPU from Nvidia and thank Google for their compute resources provided (Google Earth Engine).
5. Conclusion
We presented a novel approach for generating high- resolution, large-scale temporal tree canopy height maps using 3D U-Net model. By utilizing full 12-month
8

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Capturing Temporal Dynamics in Large-Scale Tree Canopy Height Estimation
Impact Statement
This study significantly advances forest monitoring by lever- aging machine learning to incorporate temporal dynamics, resulting in the creation of tree canopy height maps over continental scale. Forests
four-yeat span (2019-2022) play a vital role in mitigating climate change by absorb- ing nearly half of human- generated carbon dioxide emis- sions (Friedlingstein et al., 2022). However, they are in- creasingly at risk due to climate change and human activities such as deforestation and land degradation (Anderegg al., 2022). In support of global initiatives like the United Na- tions' Sustainable Development Goals (SDGs)² the Bonn Challenge", and the Glasgow Declaration effective forest management and conservation are crucial for climate adapta- tion and mitigation. Our approach significantly benefits the estimation of above-ground biomass, a critical component among the 55 essential climate variables (ECVs)"! key improvement in this research the enhanced detection of large trees, which is critical due to the power-law relation- ship between tree height and biomass. This advancement allows for more accurate biomass estimation, essential for assessing carbon stocks. Additionally, precise monitoring crucial for validating carbon credit investments in the voluntary carbon market, ensuring the effectiveness and sus- tainability of forest growth projects while reducing potential leakage effects.
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Capturing Temporal Dynamics in Large-Scale Tree Canopy Height Estimation
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A. Data Handling Details
Handling big amounts of data is a challenge. To aid data handling and still having optimal projection accuracy, we downloaded, stored and preprocessed all data in the original Sentinel-2 tiling system. The tiling system is based on the UTM projections system but divides every UTM zone in 100 km 100 km tiles. Each UTM zone has its own projection 12
Sentinel-1 images are created with Google Earth Engine (GEE) directly in their corresponding projection and compressed with I.ZW (the standard compression in GEE). Sentinel-2 images are distributed via the Copernicus AWS via .SAFE- archive format. We extract all needed bands in JPEG2000-format and build virtual raster files for combined access. JPEG2000 has the big advantage of being an extremely storage efficient, yet applies the compression on image-level, so windowed access not possible.
Each sample of the final dataset is stored in a zip-compressed archive to further save storage.
When doing inference. directly loading the entire tile and doing inference is not feasible, as data loading takes long time due to the need to be decompressed. As this would lead to long GPU idle times, we use different approach. We separate the workload onto CPU- and GPU-nodes, where CPU-nodes load the data from the slow storage, decompress it multi-threaded and save it in a binary format on fast-access storage. The GPU-node then loads the fast binary and does inference. For each GPU-worker we start multiple CPU-workers to remove GPU idle time.
B. Ablation Studies
In the main paper we took several decisions without justification. Here we provide experimental ablation studies to underpin these conclusions.
B.I. Additional Sentinel-2 Bands
Sentinel-2 captures bands in resolutions of 10 m, 20 m and 60 m and in this study we included all (except for B10, which is only for clouds). However, both 60 m bands have wavelength, where direct knowledge transfer to this application is unknown: B01 measures coastal aerosols and B09 captures the density of water vapors. Our experiments show that both bands do not significantly increase the performance, neither do they decrease the performance. Further analysis is needed, but both bands are candidates to be removed from the set of input channels. Table reports the results on the validation part of our dataset (L1 > 15 m refering to the L1 loss for all labels that exceed 15 m)
Table 6. Comparison of error metrics with and without spectral bands B01 and B09.
Configuration
LI (m)
Ll 13 m (m)
LI > 20 (m)
> 25 m (m)
L1 > 30 m (m)
L2 (m)
Without B01 and B09
1.991 # 0.002
4.837 0.008
5.476 0.010
7.384 0.008
11.406 0.004
22.281 = 0.037
Including B01 and B09
1.992 = 0.003
4,830 + 0.014
5.460 + 0.015
7.364 # 0.029
11.384 = 0.031
22.277 = 0.053
B.2. Time-Series: Seasonal pPatterns
Although our main paper shows that a time-series clearly outperforms the baseline setting of applying a median aggregation to the imput data, we want to dive deeper in how the model leverages e.g. differences across the monthly images. We present 3 experiments: activation patterns for different months, performance for different forest types (with different phenological patterns) and ablation between using only summer/winter months or both.
B. ACTIVATION PATTERNS FOR DIFFERENT MONTHS
We use Guided Attention (Springenberg et al., 2014) to visualize the activation patterns across months for different patches (cf. Figure 7. We observe varying activation strengths across months and patches, suggesting the model processes temporal information differently by location. However, further research would be needed to confirm this hypothesis.
B.2.2. PERFORMANCE ON DIFFERENT FOREST TYPES
For more details, we refer to: https://seette http///entii/t/// wiki coperni cus. Bhtpps: http////aatttteeeeeeeeeeeee ne. googl com
wuw.eu/wee..eeeeee su/web/ss s2 wwwpss/prodduts..
com
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Capturing Temporal Dynamics in Large-Scale Tree Canopy Height Estimation
January
February
March
April
May
June
July
August
September
October
November
December
Figure 7. The activation patterns differ across months signaling that some images are more important. i.e. contain more information, than others. Further, there no pattern between multiple patches.
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Capturing Temporal Dynamics in Large-Scale Tree Canopy Height Estimation
Table 7. Comparison across tree types for different models.
We evaluated our model separately broadleaf and conif-
erous forests using the Copernieus Land Monitoring Ser- vice Forest Type Map (2018) (Copernicus Land Moni- toring Service, 2021). These forest types show different seasonal patterns broadleaf forests have distinct leaf- on/off periods while coniferous forests maintain constant canopy. Table 7 reports our findings.
MODEL
BROADLEAF
CONIFEROUS
MAE (M)
MAE (M)
LANG ET AL. LIU ET AL.
5.44
5.11
7.01
6.91
PAULS ET AT.
5.30
4.85
TOLAN ET AL.
10.43
11.73
TURUBANOVA ET AL.
8.60
8.43
B.2.3. SEASONAL INFORMATION VARIATION
OURS
4.57
4.11
To investigate the benefits of seasonal information inde- pendently from data volume (Note: the number of training labels remains identical for all variants), we conducted an ablation study comparing three models trained on different 4-month subsets: Winter (Nov-Feb), Summer (Jun-Sep) and Mixed (Jan-Feb, Aug-Sep).
Table 8. Seasonal variation in performance using Huber Loss (in
The results in Table show that using only summer/leaf- on months is superior using only winter/leaf-on off months, however mix of winter and summer months yields better validation performance than both individually.
meters) for different model variants.
MODEL VARIANT
HUBER Loss (M)
WINTER (Nov-FEB)
1.169 0.003
SUMMER (JUN-SEP)
1.130 0.002
MIXED (JAN-FEB, AUG-SEP)
1.122 0.002
C. Analysis
Although our primary goal was to develop an openly available model that others can use for their own analyses, here we demonstrate the model's temporal capabilities. We analyzed potential deforestation events by tracking pixels for which the corresponding height decreased from above 8m below 5m between years. The affected area increased from 9747.9 km3 between 2019 and 2020 and 7729.1 km between 2020 and 2021 15942.5 km between 2021 and 2022.
15

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Capturing Temporal Dynamics in Large-Scale Tree Canopy Height Estimation
D. Additional Figures
Figure 8. Tree canopy height map for central Europe for 2020.
16

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Capturing Temporal Dynamics in Large-Scale Tree Canopy Height Estimation
Google Maps
Ours
Lang al.
Liu et al.
Tolan et al.
Pauls et al.
Turubanova al.
Figure 9. Comparison of different tree canopy height products, including Lang et al. (2023); Liu et al. (2023); Tolan et al. (2024); Pauls
(2024); Turubanova (2023). first column shows nigh-resolution image from Google Maps.
17


Reviews:
[{"review_id": "tczABDk8UA", "reviewer": "Reviewer_1141", "summary": "This paper introduces a novel deep learning approach for generating high-resolution, large-scale temporal canopy height maps across Europe using satellite imagery, specifically leveraging Sentinel-2 time series data and GEDI LiDAR measurements as training data. The proposed method significantly improves accuracy and resolution, delivering consistent 10-meter spatial resolution canopy height predictions from 2019 to 2022, which allows for the tracking of temporal dynamics such as deforestation and forest growth. By using a 3D U-Net model architecture with monthly temporal stacks, the model effectively captures seasonal variations and geolocation shifts, demonstrating superior performance compared to existing approaches, especially in accurately estimating the height of tall trees critical for carbon stock assessment and ecological analysis. In general, this paper makes a great contribution in the training dataset and canopy height data products.\n\n## Update after Rebuttal\nThe authors' response to my questions fully clarified my concerns. This is a good paper that utilizes well-established methods in an application area that resolves practical questions in sustainability. Thus, this paper is a good fit for ICML's Application-Driven Machine Learning track and deserves acceptance.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thorough review and for acknowledging the contributions of our work. Let us address your concerns and questions one by one.\n\n> More references to remote sensing timeseries understanding literature such as [1] can be discussed.\n\nThank you for the suggestion. We have added additional references to our manuscript.\n\n> Although this paper mainly focuses on methodology and dataset [...]\n\nWe agree this is important. While our main focus is providing an openly available model for others to analyze, we did conduct some initial analysis: By tracking pixels where height decreased from >8m to <5m between years (indicating potential deforestation), we found affected areas of 9,747.9 km² (2019-2020), 7,729.1 km² (2020-2021), and 15,942.5 km² (2021-2022). We have added these findings to our manuscript.\n\n> Line 152: What’s the reason for including coastal aerosol (B01) and water vapour (B09) bands?\n\nWe decided to include all L2A (bottom-of-the-atmospohere) information, which includes all bands except for B10. We agree that B01 and B09 might not necessarily be relevant for the task at hand. To analyze their impact, we ran additional experiments to assess their importance. The following table reports the results on the validation part of our dataset (L1>15m refering to the L1 loss for all labels that exceed 15m):\n\n| Configuration         | L1 (m)         | L1>15m (m)     | L1>20m (m)     | L1>25m (m)     | L1>30m (m)     | L2 (m)         |\n|-----------------------|----------------|----------------|----------------|----------------|----------------|----------------|\n| Without B01 and B09   | 1.991 ± 0.002 | 4.837 ± 0.008 | 5.476 ± 0.010 | 7.384 ± 0.008 | 11.406 ± 0.004 | 22.281 ± 0.037 |\n| Including B01 and B09 | 1.992 ± 0.003 | 4.830 ± 0.014 | 5.460 ± 0.015 | 7.364 ± 0.029 | 11.384 ± 0.031 | 22.277 ± 0.053 |\n\nAs it can be seen, B01 and B09 only have little impact on the results and are, hence, candidates to be removed from the set of input channels. We will discuss these findings in the updated version of our manuscript.\n\n> Line 185: Could the authors clarify the sparsity of the labels? What percentage of the pixels have a canopy height label derived from GEDI?\n\nGEDI measures roughly 4% of Earth's surface. For our 256x256 pixel training samples, only about 100 pixels (0.15%) have usable GEDI labels from the same year, due to noise and the need to match measurements temporally with satellite imagery.\n\n> Line 197: How are 2.56 km × 2.56 km patches created? Are they created by buffering GEDI point measurements?\n\nWe first make sure to only take image patches from our training areas to not have an overlap with the validation patches. We then randomly select a 2.56km x 2.56km area and load all GEDI measurements within that area. Since we only have the coordinates of the GEDI measurements, we assign them to the closest Sentinel-pixel. We hope that this clarifies your questions. Please let us know if that is not the case.\n\n> Line 199: Do “month images” refer to month composites or one image selected within a month? If the latter, could the authors clarify the reasons for not using monthly composites?\n\nWhen creating the 12-months image stack, we select one of the images within each month, namely the one with the least amount of cloud cover. We decided not to use monthly composites, following Wolters et al., who showed that not using composites allows the model to learn finer details, possibly due to small geolocation shifts in the satellite images.\n\n> Line 207: Is smoothing applied before calculating validation metrics or just for the final mapping visualization?\n\nSmoothing is only applied for the visualizations.\n\n> My only concern is the possible spatial autocorrelation in the validation dataset [...]\n\nThank you for raising this point. Indeed, there are spatial correlations between the different spatial areas. Note, however, that the learning scenario can be seen as a transductive learning setting, where one already has access to the (test) input images (but not the ground-truth labels). From that perspective, it is even valid to include the geolocation of an image stack as input. Note that the overall goal is to fill the remaining \"gaps\" that are not covered by GEDI groundtruth labels. We hope that this answers your question. Please let us know if there are still remaining concerns from your side.\n\n> Have the authors considered other architectures for remote sensing timeseries such as [1]?\n\nWe have considered other segmentation architectures and backbones, but we found that the 3D extension of the U-Net architecture to work surprisingly well. U-Nets are efficient and in our setting, yield good results. We will consider exploring more complex architectures in future work.\n\nWe hope to have clarified all concerns and questions, please let us know if further clarification is needed. Thanks!", "claims_and_evidence": "The authors present three central claims, all of which are supported by quantitative and qualitative results:\n1. **A model capable of tracking forest height changes**: The paper features the first 10 m resolution temporal canopy height map of the European continent for 2019–2022, as shown in the Earth Engine app. Thus, comparing those annual maps can be used to track changes in forest height. In addition, Figure 6 provides additional qualitative examples of change tracking.\n2. **More accurate measurements and finer spatial details than previous studies**: The performance of the model against baselines is comprehensively evaluated through the experiments in 4.3.\n3. **12-month timeseries is helpful than a single composite**: Table 2 and Table 3 substantiate this claim.", "methods_and_evaluation_criteria": "The evaluation protocol of this paper conforms with prior works in forest canopy height estimation. The proposed method (3D Unet for mapping tree height from satellite timeseries) also makes sense conceptually.", "theoretical_claims": "N/A", "experimental_designs_or_analyses": "I reviewed the experiment design, especially the validation dataset. My only concern is the possible spatial autocorrelation in the validation dataset since the validation dataset is generated by randomly selecting tiles. Is it possible that two very close tiles share similar canopy heights?", "relation_to_broader_scientific_literature": "The paper advances the field of tree canopy height estimation by introducing a novel deep learning model capable of producing temporal canopy height maps at 10 m resolution over large spatial scales. Unlike prior studies that mainly focused on single-year canopy height predictions, this work addresses the critical gap of modeling temporal dynamics, crucial for tracking ecological changes and carbon stocks. It builds upon previous research by using Sentinel-2 monthly image stacks instead of aggregated median composites, thereby leveraging seasonal vegetation patterns and subtle geolocation shifts to enhance accuracy. Furthermore, this approach significantly surpasses existing methodologies, such as those of Liu et al. (2023) and Tolan et al. (2024), especially in accurately estimating taller trees, which are essential for precise biomass estimations.", "essential_references_not_discussed": "More references to remote sensing timeseries understanding literature such as [1] can be discussed.\n\n[1] Tarasiou, M., Chavez, E. and Zafeiriou, S., 2023. Vits for sits: Vision transformers for satellite image time series. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10418-10428).", "other_strengths_and_weaknesses": "This paper is well written and has great contributions in its training dataset and the final data product produced. Please see other comments and questions for the weaknesses.", "other_comments_or_suggestions": "Although this paper mainly focuses on methodology and dataset, I encourage the author to include scientific conclusions about forest health or deforestation of the European continent, if any, from analyzing the trend of yearly forecast canopy height maps generated by the model.", "questions_for_authors": "1. Line 152: What’s the reason for including coastal aerosol (B01) and water vapour (B09) bands?\n2. Line 185: Could the authors clarify the sparsity of the labels? What percentage of the pixels have a canopy height label derived from GEDI?\n3. Line 197: How are 2.56 km × 2.56 km patches created? Are they created by buffering GEDI point measurements?\n4. Line 199: Do “month images” refer to month composites or one image selected within a month? If the latter, could the authors clarify the reasons for not using monthly composites?\n5. Line 207: Is smoothing applied before calculating validation metrics or just for the final mapping visualization?\n6. Line 240: Have the authors considered possible spatial autocorrelations in the validation dataset? For example, two randomly selected points can have similar canopy heights.\n7. Have the authors considered other architectures for remote sensing timeseries such as [1]?\n\n[1] Tarasiou, M., Chavez, E. and Zafeiriou, S., 2023. Vits for sits: Vision transformers for satellite image time series. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10418-10428).", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "4IalTIe66C", "reviewer": "Reviewer_sUWg", "summary": "The article describes a method for calculating canopy height using satellite data and reference values from GEDI LiDAR. The authors propose the use of a UNet network for regression. Using multispectral Sentinel images, they provide canopy height estimates for Europe between 2019 and 2022. Their R² is 0.819. When they only consider labels exceeding 7m, their R² is 0.591.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your positive and thorough review. Let us address your concerns and questions in detail.\n\n> How do you know that you are looking at trees (as opposed to buildings, for instance)?\n\nThat is a very good and true observation, indeed, given that GEDI measures the height of all objects, we cannot tell whether we are estimating a tree or a building: buildings are also measured at some height, therefore in cities one can see height prediction even in the absence of trees. This however is common practice in the remote sensing community. Canopy height maps are mainly used for forest monitoring and carbon stock estimation and both applications apply a forest mask before further use, which could be itself generated by a segmentation model; which is however a different problem we do not address here. While this is common practice, we will make this more clear in the paper to avoid confusion.\n\n> why is Sentinel-1 not mentioned in the abstract?\n\nThat was not intentional, thank you for pointing it out. We have updated our manuscript accordingly.\n\n\n> Please explain why sqrt{\\text{MSE}} \\neq \\text{RMSE}?\n\nWe individually calculate the MSE and RMSE for each of the 1,500 validation patches and average them afterwards (weighted average based on the number of labels in each patch). This is why there can be a difference. Thank you for this remark. We will provide additional explainations in our manuscript.\n\n\n> Even though the authors do not provide their weights or datasets (they state that they will do so upon acceptance of their publication), the code provided appears reasonable.\n\nPlease note that the predictions can be viewed and downloaded from the Google Earth Engine (GEE) website linked in the paper. We are happy to share all the code to reproduce our results upon acceptance. Sharing terabytes of data in an anonymous way right now is challenging.\n\n> I would recommend training, validating, and testing in different years, in non-overlapping.\n\nWe tested this approach under the \"-2020\" setup, where we train only on 2020 data and evaluate on data from 2019, 2021 and 2022 (cf. e.g. Table 3 in the paper). We have made this more explicit in our manuscript, thank you.\n\n\n> A search yields the following articles related to the problem that are not mentioned: Satellite Image and Tree Canopy Height Analysis Using Machine Learning on Google Earth Engine with Carbon Stock Estimation\n\nThank you very much. We have added the reference to the background section of our manuscript.\n\n\n> what is the normalizing divisor in Table 1? Where does it come from?\n\nWe found it beneficial to rescale the data to a range that is more suitable for the model (i.e., we divide the input data by this divisor). To that end, we manually inspected the data to find out value ranges that contain valuable data, in particular because standard normalization did not work due to problem with atmospheric distortions and cloud cover.\n\n> provide R2 in Table 2. (no need to include both MSE and RMSE)\n\nThank you for the suggestion, we have added R2 to Table 2. \n\n\n> seperate, Appendix A\n\nFixed, thank you!\n\n\n> What are the sources of error when estimating tall trees?\n\nAlthough we do not know the exact reason, we suspect that it has to do with the following two reasons:\n1. Due to the natural distribution of trees in Europe, tall trees are less common, creating a skew in the label distribution.\n2. Tall trees naturally have a higher canopy density (e.g. more leaves, branches, etc.), which leads to a higher fraction of LIDAR photons/measurements not penetrating the canopy. In that case, the photons do not reach the ground and we do not have a usable measure of the tree height (this filtering is already applied by GEDI, not by us).\n\n\n> Why not use stratified evaluation based on different forest types?\n\nThank you for this suggestion. While our paper focused on overall performance metrics, we have now included a detailed analysis comparing broadleaf and coniferous forests (see our response to reviewer qZ94). We welcome your input on additional forest categories that would be valuable to evaluate.\n\n\n> What post-processing steps were used to ensure temporal consistency?\n\nWe use a quadratic-spline approach to smooth the predictions over time, but only for visualization purposes.\n\nWe hope to have addressed all your remarks. Thank you again. If you have any further questions or concerns, please let us know.", "claims_and_evidence": "The authors provide an extensive discussion of their procedure to obtain their results. This discussion includes different options for constructing the training set, the use of various error measures, comparisons with other approaches, and the distribution of errors for different tree sizes. They also conduct a qualitative evaluation by presenting several examples.\nHowever, some observations I have include:\n*** How do you know that you are looking at trees (as opposed to buildings, for instance)?", "methods_and_evaluation_criteria": "The authors construct their database, which consists of Sentinel-1 and Sentinel-2 images as predictors and GEDI LiDAR as the reference value (why is Sentinel-1 not mentioned in the abstract?). They then train their UNet using different error measures (*** Please explain why MSE≠RMSE\\sqrt{\\text{MSE}} \\neq \\text{RMSE}?).\nEven though the authors do not provide their weights or datasets (they state that they will do so upon acceptance of their publication), the code provided appears reasonable.", "theoretical_claims": "The article does not include a relevant theoretical proof or one that needs to be tested. However,  I would recommend training, validating, and testing in different years, in non-overlapping.", "experimental_designs_or_analyses": "The article does not provide weights or data to verify the claims, although the authors have stated that they will do so once the paper is accepted. The included code appears to be reasonable.", "relation_to_broader_scientific_literature": "The paper\n1. introduces a multi-temporal approach to canopy height estimation. Other studies have used \n optical data (Schwartz et al., 2024) \n2. utilices Sentinel-2 time series instead of median composites( Pauls et al., 2024)\n3. develops a 3D U-Net model that improves performance over prior 2D approaches \n\n4. achieving state-of-the-art results in canopy height prediction at 10m resolution.\n(Liu et al., 2023; Pauls et al., 2024)\n5. provides publicly available code, but not weigths nor data.", "essential_references_not_discussed": "A search yields the following articles related to the problem that are not mentioned:  \n\n1. **Satellite Image and Tree Canopy Height Analysis Using Machine Learning on Google Earth Engine with Carbon Stock Estimation**", "other_strengths_and_weaknesses": "This article presents an important application to the study of forests. The authors scaled their solution to cover Europe, and their results appeared to advance the state of the art.\nHowever, I am uncertain about how their study specifically returns to tree height rather than the height of objects in general, such as buildings.\nThe authors do not provide weights or data, although they state that they will make it available upon acceptance of the paper. They provide code that seems to be well structured. The details provided would allow for replicating their approach.", "other_comments_or_suggestions": "***what is the normalizing divisor in Table 1? Where does it come from?\n\n\n*** provide R2 in Table 2. (no need to include both MSE and RMSE)\n\n\n\n*** I would recommend training, validating and testing in non-overlapping different years\n\n\n\n*** seperate, Appendix A", "questions_for_authors": "1. How does the model distinguish between trees and other tall objects (e.g., buildings)?\n\n\n\n2. What are the sources of error when estimating tall trees?\n\n\n3. Why not use stratified evaluation based on different forest types?\n\n \n\n4. What post-processing steps were used to ensure temporal consistency?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "X3B1MN9ULz", "reviewer": "Reviewer_qZ94", "summary": "The paper presents an approach for creating large-scale temporal tree canopy height maps using satellite imagery. The main contributions include a deep learning model (3D U-Net architecture) that can track forest height changes across Europe from 2019-2022 at 10m spatial resolution; a canopy height map of Europe for 2020; and the finding that using full 12-month time series of Sentinel-2 imagery improves performance by capturing seasonal patterns and leveraging geo-location shifts, compared to aggregated composites).", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your detailed review. In our manuscript, we proposed seasonal variation and geolocation shifts as possible factors influencing the model's performance. We agree that some of our statements have been to explicit from a environmental perspective (e.g., to make use of the geolocation shifts given in Sentinel-2 time series data to improve model performance or the use of time series data to capture seasonal patterns). We regret these unprecise statements. We have revised the manuscript to present these as hypotheses rather than definitive claims. Below, we include further experiments and analyses that explore these factors in more detail. We hope that our comments and additional experiments address your concerns.\n\n### Seasonal Pattern Analysis: How does the model specifically utilize seasonal information? Could you provide analysis showing:\n> \n> a) Model attention/activation patterns across different seasons\n\nThe figure in https://ibb.co/1f8zh2gR shows activation patterns across months for different patches using Guided Attention [1]. We observe varying activation strengths across months and patches, suggesting the model processes temporal information differently by location. However, further research would be needed to confirm these hypotheses.\n\n[1] Striving for simplicity: The all convolutional net.\n\n> b) Performance comparison between different types of forests that show different seasonal patterns\n\nWe evaluated our model separately on broadleaf and coniferous forests using the Copernicus Land Monitoring Service Forest Type Map (2018). These forest types show different seasonal patterns - broadleaf forests have distinct leaf-on/off periods while coniferous forests maintain constant canopy. The metrics below show our findings:\n\n|Model|Broadleaf MAE (m)|Coniferous MAE (m)|\n|-|-|-|\n|Lang et al.|5.44|5.11|\n|Liu et al.|7.01|6.91|\n|Pauls et al.|5.30|4.85|\n|Tolan et al.|10.43|11.73|\n|Turubanova et al.|8.60|8.43|\n|**Ours**|**4.57**|**4.11**|\n\n\n\n> c) Ablation study isolating seasonal pattern benefits from general data volume benefits\n\nNote that the number of training labels remains identical for all variants. To investigate the benefits of seasonal information independently from data volume, we conducted an ablation study comparing three models trained on different 4-month subsets:\n\n- Winter (Nov-Feb)\n- Summer (Jun-Sep) \n- Mixed (Jan-Feb, Aug-Sep)\n\nThe results below show that using a mix of winter and summer months yields better validation performance.\n\n|Model Variant|Huber Loss (m)|\n|-|-|\n|Winter (Nov-Feb)|1.169 ± 0.003|\n|Summer (Jun-Sep)|1.13 ± 0.002|\n|Mixed (Jan-Feb, Aug-Sep)|1.122 ± 0.002|\n\nThank you for this comment. We will provide these additional findings in the updated version of our manuscript.\n\n\n### Geolocation Shift Benefits: Can you provide direct evidence that the model leverages Sentinel-2 geolocation shifts? Specifically:\n> \n> a) Comparative analysis with models that cannot use shift information \n> c) Quantification of improvement specifically attributable to shift utilization\n\nDirect analysis of geolocation shifts is challenging as they are inherently embedded in raw satellite data. However, our ablation study (Table 3) shows superior performance using raw vs composite data. Note that although the amount of input data changes, we keep the number of training labels constant.\n\n> b) Examples showing improved edge detection or spatial detail\n\nWe provide a comparative figure (https://ibb.co/WpHqStqM) demonstrating enhanced edge detection across three model variants: 2D-Composite, 2D-Stack, and 3D-Stack. Additional examples of improved edge detection can be found in Figures 1, 4, and 8, as well as in our interactive Google Earth Engine application.\n\n\n### Growth Detection Validation: How reliable is the model at detecting forest growth? Please provide:\n> \n> a) Validation using known growth areas\n\nWhile our model's ability to detect forest growth is constrained by the 4-year observation period, we observe clear growth signals in forest plantation regions like the Le Landes forest plantation in France (search of \"Garein\" in our GEE app).\n\n> b) Comparison with ground measurements over time\n\nWe collected LiDAR data from the Vosges forest in France for 2020 and 2022 and analysed the measured vs predicted growth (https://ibb.co/YTbkXfpf). Our model is able to predict the growth of the trees, however the model uncertainty is high, which leads to \"heavier tails\" than what was measured. We have added these results to our manuscript.\n\n> c) Analysis of minimum detectable growth rate\n\nGiven our model's MAE of 4.76m and GEDI's uncertainty of 0.98m, we estimate that reliable growth detection requires changes of at least 5m over the observation period, varying by region and forest type.\n\nWe have also revised the paper about computational requirements, model performance and robustness across different forest types. Please let us know if you need any additional clarification.", "claims_and_evidence": "* Capturing seasonal patterns: The paper claims that using full 12-month time series of Sentinel-2 imagery improves performance by \"capturing seasonal patterns\" (compared to using aggregated composites).\n    * The paper lacks direct analysis of how the model actually uses seasonal information, with examples showing different predictions in leaf-on vs leaf-off seasons. While the paper shows that using 12 months performs better than composites, they don't isolate whether this improvement is due to seasonal patterns or simply having more data points.\n    * To properly support this claim, the paper should include analysis of the model's attention to seasonal changes, performance comparisons across seasons, and demonstrations of different behavior for deciduous vs evergreen forests. Explicit testing is needed to show that the improvement comes from seasonal information rather than just more data points. Visualizations or examples showing how the model uses seasonal information, along with ablation studies isolating the impact of seasonal patterns, would significantly strengthen the evidence for this claim.\n\n\n* leveraging geo-location shifts: the paper claims that by processing a stack of 12 monthly Sentinel-2 images concatenated with an aggregated Sentinel-1 composite, the method leverages geolocation offsets in Sentinel-2 imagery.\n    * The paper lacks visualization or quantitative analysis demonstrating how the model uses geolocation shifts, including examples showing improved edge detection or fine spatial details that could be attributed to leveraging these shifts. On the contrary the spatial resolution is lesser than Liu et al. The improved performance could be due to other factors like increased data volume or temporal information, rather than specifically leveraging geolocation shifts. \n    * To properly support this claim, the paper should include an ablation study isolating the impact of geolocation shifts from other factors, with metrics or measurements showing the degree of improvement that can be attributed to leveraging geolocation shifts. Providing examples showing enhanced edge detection or spatial detail due to this technique will strengthen the claims in this paper.", "methods_and_evaluation_criteria": "The methods and evaluation criteria employed in the paper are generally appropriate for the problem of large-scale canopy height estimation, with some notable strengths and limitations:\n\nStrengths:\n\n* Use of GEDI LiDAR data as ground truth aligns with standard practices in the field and comprehensive comparison with existing methods using multiple metrics (MAE, MSE, RMSE, R²)\n* Inclusion of both quantitative metrics and qualitative visual comparisons with use of high-quality ALS data for additional validation of tall tree detection\n\nLimitations:\n\n* Temporal validation relies heavily on detecting deforestation, with limited validation of growth detection\n* While multiple metrics are used, they don't specifically address the claimed benefits of seasonal patterns and geolocation shifts", "theoretical_claims": "Not Applicable", "experimental_designs_or_analyses": "Yes", "relation_to_broader_scientific_literature": "The paper's contributions can be placed within the context of the broader scientific literature on Forest Monitoring and Remote Sensing:\n\n* Builds upon established work using satellite data for forest monitoring\n* Advances previous single-year height mapping efforts (Lang et al., Liu et al.) by incorporating temporal imagery.", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "The paper's most significant contribution is making high-resolution temporal forest monitoring more accessible through its integration with Google Earth Engine. While individual technical components might not be groundbreaking, their combination and practical implementation represents applied machine learning for environmental monitoring.\n\nWeaknesses:\n\n* Technical Limitations:\n    * Lack of detailed analysis supporting claims about seasonal patterns\n    * Insufficient evidence for geolocation shift benefits\n    * Limited validation of temporal dynamics, especially for forest growth\n* Practical Implementation Details:\n    * Limited discussion of computational requirements\n    * Minimal discussion of model robustness to different environmental conditions", "other_comments_or_suggestions": "Not Applicable", "questions_for_authors": "1. Seasonal Pattern Analysis: How does the model specifically utilize seasonal information? Could you provide analysis showing:\n\n    * Model attention/activation patterns across different seasons\n    * Performance comparison between different types of forests that show different seasonal patterns\n    * Ablation study isolating seasonal pattern benefits from general data volume benefits\n\n2. Geolocation Shift Benefits: Can you provide direct evidence that the model leverages Sentinel-2 geolocation shifts? Specifically:\n\n    * Comparative analysis with models that cannot use shift information\n    * Examples showing improved edge detection or spatial detail\n    * Quantification of improvement specifically attributable to shift utilization\n\n3. Growth Detection Validation: How reliable is the model at detecting forest growth? Please provide:\n\n    * Validation using known growth areas\n    * Comparison with ground measurements over time\n    * Analysis of minimum detectable growth rate", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "lsE2IKSV5G", "reviewer": "Reviewer_edjr", "summary": "This paper is the first to produce a 10m resolution time-series forest height map of Europe from 2019 to 2022. The 2020 results were compared with multiple tree height studies, revealing that its accuracy is also the most reliable. The data used include GEDI, Sentinel-1, and Sentinel-2, with the model being 3D-UNet, the loss function Huber Loss, and the Adam optimizer. The paper also presents findings on temporal height changes, such as tree felling.", "questions": "", "limitations": "", "rating": 5, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thorough review and for acknowledging our work. Let us address your remarks and questions in detail below.\n\n> However, the analysis section needs further improvement. [...]\n\nWe agree that this is an important aspect. Our primary goal was to develop an openly available model that others can use for their own analyses. To demonstrate the model's temporal capabilities, we analyzed potential deforestation events by tracking pixels for which the corresponding height decreased from above 8m to below 5m between years. The affected area increased from 7,729.1 km² in 2020-2021 to 15,942.5 km² in 2021-2022, also see our answer to reviewer 1141. We have updated our manuscript accordingly\n\n> However, a key weakness is that the study primarily applies existing models and algorithms [...] \n> \n> [1] If the improved results are merely due to [...].\n\nWe would like to emphasize that our work follows the application-driven track. While pinpointing the exact source of improvement is challenging due to significant architectural differences, the results from Table 2 demonstrate three key improvements:\n\n1. Utilizing 12 monthly images rather than a single composite image (`-Composite-` vs `-Stack-`) reduces MAE by 3.6%\n2. Processing temporal data with 3D convolutions instead of 2D convolutions (`2D-Stack-` vs `3D-Stack-`) further reduces MAE by 1.4%\n3. Training on multiple years of data (2019-2022) versus a single year (2020) (`-Year` vs `-MultiYear`) reduces MAE by 6%\n     \n\n> [2] The model structure mentions, \"12 monthly Sentinel-2 images concatenated with an aggregated Sentinel-1 composite.\" [...]\n\nThe model receives 12 monthly images, each containing 16 channels - the Sentinel-2 bands for that month concatenated with the yearly median of Sentinel-1 data. So technically, the model overall receives 12*16=192 channels as input, although we do not concatenate these, but rather process them in the 3D-U-Net as (12, 16, 256, 256) image input. Regarding your second question, we do not explicitly filter clouds from the input images, but the model demonstrates robust performance even with partially cloudy inputs, maintaining high prediction accuracy. We utilize the L2A product, which includes atmospheric correction and other preprocessing steps.\n\n> [3] Why is the 'Sentinel-1 data aggregated into a single median-based image over the year' ? Is it because the data lacks clear periodicity?\n\nWhile Sentinel-1 has similar temporal resolution to Sentinel-2, we opted to use composite radar data since it reduces noise and speckle artifacts through temporal aggregation, while keeping computational costs manageable. In contrast, we found using the full temporal sequence beneficial for optical Sentinel-2 data.\n\n> [4] The model's training configuration, duration, and computational resources should be provided in detail.\n\nIn Section 3.3 of the paper, we provide detailed information about the experimental setup, including the considered optimizer, learning rate, batch size, and more. Regarding duration and computational resources, we trained the model for a duration of roughly four days on two A100 GPUs. We will add detailed information about the computational resourced used to the revision. Thank you very much for this remark.\n\n\n> [5] If the reported MAE in this paper is 4.64m, what is the accuracy of GEDI? What is GEDI's temporal accuracy? Is the accuracy upper bound for time-series forest height inversion determined by GEDI's accuracy?\n\nGEDI has a measurement resolution of 15cm but varying accuracy by surface type - overall MAE of 0.98m, with higher error in tree-covered areas (1.67m) versus grassland (0.79m) and cropland (0.57m) [PEL]. GEDI cannot reliably measure heights 0-3m and has reduced accuracy below 5m. Regarding temporal accuracy - GEDI rarely measures the same location twice, so it lacks a temporal dimension. For accuracy bounds - as GEDI provides our training labels, this likely sets an upper limit on our model's achievable accuracy. Is this what you meant?\n\n[PEL] Pronk, M., Eleveld, M., & Ledoux, H. (2024). Assessing vertical accuracy and spatial coverage of ICESat-2 and GEDI spaceborne lidar for creating global terrain models. Remote Sensing, 16(13), 2259.\n\n> [6] Can you provide some failed cases and explain why the current method cannot handle them? Additionally, for low vegetation (below 5 meters), does this study face challenges in height estimation and time-series measurement?\n\nWe provide two failure cases: 1) GEDI measurements on slopes, where terrain changes are mistaken for vegetation height differences, and 2) checkerboard artifacts in harbor areas. We will expand this analysis in the appendix. Additionally, as noted earlier, both GEDI and our model struggle with accurate measurements of low vegetation.\n\n\nWe hope to have addressed all your concerns, please let us know if further clarification is needed. Thank you!", "claims_and_evidence": "Claims (such as first 10m-time-serises map  and better accuracy) in this work are well supported.", "methods_and_evaluation_criteria": "Yes.  Make sense for the application.", "theoretical_claims": "No theoretical proof in this work.", "experimental_designs_or_analyses": "I have checked the soundness of the experimental design and analysis. I found that the experiment itself is well-structured and complete. However, the analysis section needs further improvement. For example, there is a lack of temporal accuracy analysis, phenomenon discovery, and discussion. Currently, it seems that only Figure 6 and Tab 5 present these aspects, whereas the key highlight of the paper is the temporal aspect.", "relation_to_broader_scientific_literature": "Forest height is critically important, and time-series forest height is a key indicator for assessing Earth's health and estimating carbon sink biomass. Currently, most studies focus on static forest height estimation, while dynamic inversion remains scarce. The high-resolution time-series inversion proposed in this paper fills this gap.", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "As a machine learning application in geosciences, this work is well-structured and comprehensive, with insightful results, which is a strong point.  \n\nHowever, a key weakness is that the study primarily applies existing models and algorithms without introducing innovations in machine learning methods or techniques. For instance, why did previous static inversion methods fail in this task, while this study, using the same or similar existing algorithms, succeeds? Is it due to differences in data input, or does it stem from how the time-series data is processed and integrated? A deeper analysis of these aspects would strengthen the contribution.\n\nI agree that innovation can be based on existing methods, but it is essential to explain why the current approach, without major modifications (such as in the loss function or data mining paradigm), is able to achieve what previous methods (e.g., single-year inversion) could not. This is the truly insightful aspect, rather than merely an engineering application.", "other_comments_or_suggestions": "N/A", "questions_for_authors": "[1] If the improved results are merely due to the fact that previous methods aggregated the data while this study processes them separately, the authors should explicitly state this.\n\n[2] The model structure mentions, \"12 monthly Sentinel-2 images concatenated with an aggregated Sentinel-1 composite.\" So, what is the total number of input channels? Since a year's worth of data is extremely large and contains various uncertainties (such as clouds and other artifacts), how are these issues handled?\n\n[3] Why is the 'Sentinel-1 data aggregated into a single median-based image over the year' ? Is it because the data lacks clear periodicity?\n\n[4]  The model's training configuration, duration, and computational resources should be provided in detail.\n\n[5] If the reported MAE in this paper is 4.64m, what is the accuracy of GEDI? What is GEDI's temporal accuracy? Is the accuracy upper bound for time-series forest height inversion determined by GEDI's accuracy?\n\n[6] Can you provide some failed cases and explain why the current method cannot handle them? Additionally, for low vegetation (below 5 meters), does this study face challenges in height estimation and time-series measurement?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper received divergent scores after the rebuttal. Reviewers 1141, sUWg, and edjr provided positive recommendations, whereas reviewer qZ94 maintained a weak reject rating.

Reviewers 1141 and sUWg praised the paper for its significance in generating high-resolution, large-scale temporal canopy height maps across Europe. They highlighted its innovative integration of 12-month Sentinel-2 time series data with GEDI LiDAR measurements to produce consistent 10 m resolution maps, which are properly validated both quantitatively and qualitatively. Reviewer edjr further praised the paper’s valuable contributions to time-series forest monitoring and its practical relevance for ecological analysis and carbon stock assessment. In contrast, reviewer qZ94 raised concerns regarding technical limitations and the insufficient depth of analysis supporting some of the claims. Although the authors’ rebuttal addressed several of these issues, reviewer qZ94 remained concerned about the reliance on existing methodologies.

After carefully reviewing the paper, reviews, and rebuttal, the Area Chair acknowledges that reviewer qZ94’s concern is valid. However, the extensive experimental results, comprehensive comparisons, and a well-articulated rebuttal demonstrate that the work possesses substantial technical significance and real-world impact. Based on these considerations, the Area Chair recommends acceptance, with the expectation that the authors will address the reviewers' comments in the final version of the paper.
Decision: Accept (poster)