BioMassters: A Benchmark Dataset for Forest Biomass Estimation using Multi-modal Satellite Time-series
Authors: Andrea Nascetti
RITU YADAV
Kirill Brodt
Qixun Qu
Hongwei Fan
Yuri Shendryk
Isha Shah
Christine Chung
Venue: NeurIPS 2023 Datasets and Benchmarks Poster (2023)
Keywords: Multi-Temporal
Multi-Modal
Satellite imagery
Biomass
TL;DR: A new benchmark dataset for large scale above ground biomass estimation using satellite multi-spectral and radar time series

Abstract: Above Ground Biomass is an important variable as forests play a crucial role in mitigating climate change as they act as an efficient, natural and cost-effective carbon sink. Traditional field and airborne LiDAR measurements have been proven to provide reliable estimations of forest biomass. Nevertheless, the use of these techniques at a large scale can be challenging and expensive. Satellite data have been widely used as a valuable tool in estimating biomass on a global scale. However, the full potential of dense multi-modal satellite time series data, in combination with modern deep learning approaches, has yet to be fully explored. The aim of the "BioMassters" data challenge and benchmark dataset is to investigate the potential of multi-modal satellite data (Sentinel-1 SAR and Sentinel-2 MSI) to estimate forest biomass at a large scale using the Finnish Forest Centre's open forest and nature airborne LiDAR data as a reference. 
The performance of the top three baseline models shows the potential of deep learning to produce accurate and higher-resolution biomass maps. Our benchmark dataset is publically available at https://huggingface.co/datasets/nascetti-a/BioMassters (doi:10.57967/hf/1009) and the implementation of the top three winning models are available at https://github.com/drivendataorg/the-biomassters.

BioMassters: A Benchmark Dataset for Forest Biomass Estimation using Multi-modal Satellite Time Series
Andrea Nascetti Ritu Yadav
Kiril Brodt
Qixun Qu Hongwei Fan
Yuri Shendryk
Isha Shah
Christine Chung
University of Liège
2KTH Royal Institute of Technology
University of Montreal and Novosibirsk State University 4 Imperial College of London
Dendra Systems Driven Data
Abstract
Above Ground Biomass an important variable as forests play a crucial role in mitigating climate change as they act as efficient, natural and cost-effective carbon sink. Traditional field and airborne LiDAR measurements have been proven provide reliable estimations of forest biomass. Nevertheless, the of these techniques at a large scale can be challenging and expensive. Satellite data have been widely used However, the full potential of dense multi-modal satellite time series data, in combination with modern Deep Learning (DL) approaches, has yet be fully explored. The aim of the "BioMassters" data challenge and benchmark dataset to investigate the potential of multi-modal satellite data (Sentinel-1 1 SAR and Sentinel- 2 MSI) to estimate forest biomass at large scale using the Finnish Forest Centre's s open forest and nature airborne LiDAR data as a reference. The performance the top three baseline models shows the potential of DL. to produce accurate and nigher-resolution biomass maps. The dataset and the code are available on the project website: https://nascette a. github. 1o/B
valuable tool in estimating biomass
global scale.
Introduction
Forests contain significant proportion of the world's biomass and act as carbon sinks by removing carbon dioxide from the air through photosynthesis. On the other hand, forest release carbon dioxide through wildfires, respiration, and decomposition. Estimate of biomass change enables a direct measurement of carbon sequestration or loss that can help validate carbon-cycle models and quantify the
impacts on global climate change. In order to understand how much carbon forest contains (its carbon stock) and how changes (carbon flux), important to have an accurate measure of Above Ground Biomass (AGB). Monitoring variation of forest biomass at the global, national and regional scales a crucial indicator for making informed decisions and assessing the impact of new policies for preservation and improvements of carbon sequestration. Biomass has direct influence on local, regional and even global climate, particularly on air temperature and humidity X Forest biomass data can play crucial role in understanding and predicting climate, for example in climate model initialization and testing, carbon turnover estimation, and data assimilation in carbon cycle models. Moreover, forest AGB is one of the sub-indicators of Sustainable Development Goal 15, which tracks changes in carbon stocks and assesses the impact of forest degradation, deforestation and management practices the carbon cycle. While field measurements and Airboree Laser Scanning (ALS) are effectively used to retrieve information about
"Corresponding author: nascetti @ kth.se
37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks.

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forest biomass, it's challenging and expensive to use these techniques at a large scale. Satellite remote sensing has been extensively used for AGB estimation due its ability provide spatially comprehensive information at various spatial and temporal scales. The utilization of satellite data to measure biomass can reduce the cost and resources required for forest monitoring and carbon estimation. DrivenData, in partnership with the Geomatics units of the University of Liège and KU Leuven, hosted machine-learning competition to estimate AGB in the forests of Finland. The competition aimed at developing open-source deep-learning models that can accurately estimate the AGB using multiple satellites, Participants were challenged to use yearly Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 MultiSpectral Imagery (MSI) time series inputs to estimate AGB forest biomass. In this paper we present the benchmark dataset and we analyze the performance of the top-score models.
yearly coverage
ALS Anea 2017
ArS-Aress 2018
ALS Area 2019
Arees 2020
Bearup CSM
Figure 1: Overview of airborne LiDAR yearly coverage (acquisitions during the summer period at peak of biomass)
2 Background
SAR, MSI, and LiDAR (Light Detection and Ranging) satellite sensors are all used to measure vegetation and estimate AGB, but they differ in their capabilities sense the vegetation and forest structures.
SAR sensors microwave radiation penetrate the canopy and measure the backscatter of the radiation from different vegetation layers. SAR can provide information on vegetation structure, biomass, and water content, and can penetrate clouds and operate day or night, making it suitable for monitoring vegetation also in areas with frequent cloud cover. Several studies have been performed using SAR data acquired from different sensors (e.g. TemaSSA-X. Sentinel-1, ALOS-2) operating in X, or bands [21 24 33
48
MSI sensors capture reflected sunlight at different wavelengths, which can be used to identify different types of vegetation and estimate vegetation properties such as leaf area index and chlorophyll content. MSI sensors are effective for monitoring vegetation dynamics and land use changes over large areas but are limited by cloud cover and solar illumination. Various studies have effectively utilized time series of reflectance data from Landsat to monitor the global extent and patterns of forest cover disturbance and recovery 10
18 45
To estimate AGB losses from carbon density maps, proposed Only handful of studies have used time series of MSI
methods have been discussed 6
data to model AGB over time and derive AGB changes
[45] 25 26 30 32). Sentinel-2 provides
high-resolution multispectral data that can be used to estimate AGB by analyzing the forest canopy structure and green vegetation cover.
LiDAR sensors emit pulses of laser light and measure the time it takes for the light to return after bouncing off the vegetation, providing a detailed 3D representation of the vegetation structure. LiDAR can estimate the height and volume of individual trees, as well as vegetation canopy height, density,

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and biomass 44. However, LiDAR is limited by its high cost and lower spatial coverage compared SAR and MSI sensors. The recently launched Ice. Cloud, and Land Elevation Satellite (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI) have allowed for the study of forest height and biomass on both local and global scales 13
29
12 31.
The combination of satellite data from different platforms, such as those listed above, with forest plots (i.e. ground-based measurements of tree parameters such as diameter, and height), can improve the accuracy of AGB estimation providing a comprehensive understanding of forest structure and biomass. Several studies have shown that using data from multiple sensors more effective in predicting forest biomass than using data from a single sensor [24]. However, incorporating multi- source data into an improved estimation of forest AGB requires the use of advanced techniques. In order to enhance the capacity of the biomass model to accurately estimate nonlinear relationships, various machine learning techniques
including modern machine learning approaches (20 15 22 42).
have been employed for remote sensing-based estimation of forest AGB (28, 27]. Decision tree, K-nearest neighbor (KNN), Artificial Neural Network (ANN), and Support Vector Machine (SVM) among the methods utilized for this purpose. Notably, studies in the past have highlighted the exceptional performance of decision tree-based algorithms, including random forest and gradient boosting, in biomass estimation modeling [9] / [] However, the full potential of DL algorithms for biomass estimation from satellite data has not been fully explored due to the lack of reliable benchmark datasets and the underutilization of dense satellite time series, such as the ones acquired from Sentinel-1 and Sentinel-2 satellites.
3 Benchmark Dataset
The data challenge, recently hosted by Driven Data in collaboration with the Geomatics Units of the University of Liège and KU Leuven, presents a new seep-learning-redyy benchmark dataset for es- timating the AGB using multi-temporal Sentinel-1 and Sentinel-2 data. The benchmark dataset is publically available at For detailed information on data format and downloading instructions, please refer the supplementary material. Some technical details regarding the data collection are reported in Table [
(doi:10.57966) (0..57677hf/1009))
In the following subsections, we present the characteristics of AGB reference data, input satellite imagery and the pre-processing steps.
Table 1: Data Specifications.
Reference
Modality
Modality 2
Sensor
ALS
SAR
MSI
Source
NLS Finland
Sentinel-1 (ESA)
Sentinel-2 (ESA)
Resolution
16x16 grid, 5ppm
20m
10m, 20m, 60m
Channels
VV, VH (ASC, DSC)
Except I, 9, 10
Coverage
8.5 million hectares
Time Frame
Sep 2016 Aug 2021
Data Size
310000 patches (# 13000 ref. 12 months 2 modalities)
3.1 Reference Labels
The reference AGB data is based on Airbome LiDAR campaigns performed by the Finland Forest Centre [2] in cooperation with the National Land Survey (NLS) of Finland. Airborne LiDAR and aerial imagery are acquired following six-year cycle to cover the entire country. The aerial imagery acquired every three years: the first time in the same year as LiDAR and the second time in the latter half of the six-year cycle. LiDAR acquisitions are performed from an altitude of 1.5-2 km providing a point density of at least 5 observation points per square meter (ppm). The aerial imagery has GSD of 0.4m and is used to identify tree species. The Finnish Forest Centre conducts annual surveys of approximately 22 remote sensing areas in different parts of the country, covering a total of 3.5-4 million hectares of forest (see highlighted areas in Figure I). The LiDAR and acrial imagery
3

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combined with field data to provide corresponding characteristics of laser point data and pixel values of aerial imagery for each sample plot. Each inventory area is measured using 800 sample plots. which allows tracking of forest variations and facilitates the interpretation process. The resulting data available as free and open forestry inventory data under CC-BY-4.0 license. The data is organized in polygon layer with 16 m 16 m grid and includes tree attributes, such as tree species, number of trees, relative percentage within the grid, trunk diameter, tree height, and more. The average Root Mean Square Error (RMSE%) of the tree attributes (total stem volume, basal area, and diameter) is around 8%. The cartographic reference system used is the TTRS--TM3FINN (EPSG:3067). AGB is derived for each cell from the tree attributes using calibrated allometric equations for different types The AGB values are expressed as tonnes (1) per cell (i.e. pixel).
of trees 36, 37..
3.2 Satellite Data
The satellite multi-modal time series considered as input data are respectively acquired by Sentinel-1 SAR and Sentinel-2 MSI sensors.
Satelitte Data
Senthnel-1 Imagery
Sentinal-2 C LSI L2A imagery
Reference Data
Forest Plots
Aiboome LDAR Survey
Themmal Noize removal, radiometic calibration & Temrain Conection
Piselo-wiss cloud probability mask (Senthnll-2 cloud
Investory Dalabsa (18x1 tm Gridy
Spetisl Temporal (1 (1-yaar data belore AGB))
Sparia & Temporal filtering (1-year data before AGB)
Allometic Equation
Ascending and Descending monihyy temporal averiging
Less dloudy monthly Image
Reference AGB
Reprojection and registration reference AGB
Reprojcction registration to reference AGB
Sample Stralegy and banshmark and cosmbbl
Area Rendom Sampling
AGB Label Chips
VV VH & das monthly (12m 258x256)
S2 monthly less-cloudy (12m 11bandd 250x256)
(2562256 upsampled 10m)
Figure 2: Overview of the data and corresponding processing steps to assemble the benchmark dataset
The Sentinel-1 mission captures C-band SAR images at a 20 m resolution with dual polarization (HH+HV and VV+VH) capability. The pre-proeeee preppecessiing of Sentinel-1 images involves the conversion of backscatter coefficients (a) to decibels via log scaling (10 log())), and the removal of thermal noise, radiometric calibration, and terrain correction. We considered all dual-band VV+VH scenes acquired in interferometric wide swath mode during given period, separated into ascending (ASC) and descending (DSC) passes due to the incidence angle's ' influence on the backscatter coefficient. Backscatter coefficients lower than -25 dB were masked to remove no-signal noise. Monthly mean images of VV and VH polarizations were computed separately for both ascending and descending orbits using pixel-wise temporal averaging to reduce SAR speckle noise. The resulting four bands, namely ASC VV, ASC VH, DSC VV, and DSC VH, were saved in a single geotiff file, which was reprojected and co-registered with respect the reference AGB area.
The Sentinel-2 mission captures optical images with 13 spectral bands at different spatial resolutions (Band 2, Band 3, Band 4, and Band 8 provided at 10 resolution and the remaining bands at 20 m or 60 m resolution). Bands 1, 9, and 10 were excluded because they contain atmospheric information not relevant to AGB estimation. Level-2A data representing Bottom of Atmosphere (BoA) reflectance

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was chosen over Top of Atmosphere (ToA) Level-IC data for the analysis. The conversion from ToA to BoA data is performed using the ESA software sen2cor[4]. Moreover. the pixel-wise cloud probability mask is generated using the sentinl2-clouud-eteetn library [3] Since the overall cloud percentage reported in the metadata not reliable for evaluating local cloud coverage, we utilize the cloud probability mask generated by the sentinel2-cloude -loudddeeee library to calculate the local percentage of clouds for each reference AGB chip. This approach aims to identify the image with the least amount of clouds, given that the AGB chips only cover small portion of the entire Sentinel-2 image footprint.
All satellite data were pre- processed using Google Earth Engine (GEE), a cloud-based platform known for its capabilities in analyzing large amounts of geospatial data in timely manner [16]. Several
studies have highlighted the potential of GEE for geospatial big data analysis [35] 49
23 47 17 46].
GEE provides direct access to analysis-ready data cubes for Sentinel-1 1 SAR and Sentinel-2 optical data, making proccesssing steps for both data modalities readily available.
3.2.1 Sampling Strategy and Data Assembly
An overview of the benehmark data preparation workflow for generating the training and test sets using Sentinel-1 SAR, Sentinel-2 MSI, and AGB reference data derived by airborne LiDAR measurements presented in Figure 2 The dataset was generated by sampling the available LiDAR survey areas (see Figure T using stratified strategy. Approximately 2.5k non-overlapping patches (or image chips) were obtained each year, for total of nearly 13000 patches covering the entire Finland. Each reference patch covers different (2560 x 2560) square meter area of forest and represents the yearly peak of AGB (the LiDAR surveys are measured during the summer: July August). Note that O values in this dataset represent areas with zero biomass (no forest). Low vegetation land covers, such as grasslands and shrublands are not included. The bounding boxes corresponding to reference patches imported in GEE as reference polygons to collect the satellite imagery. For each reference AGB chip, a full year's 's worth of monthly Sentinel-1 and Sentinel-2 images for that area are collected, from the previous September the most recent August. For example, for chip from 2020, monthly satellite data is provided from September 2019 to August 2020. The leading idea of using the previous year's time series is to have several images measure the vegetation phenology.
Each chip contains a total of 24 input images, with 12 from each satellite source. However, due to satellite data outages (e.g.. in December 2019). not all chips have coverage for every month. The satellite image patches are provided as TIFF files and any associated geolocation data has been removed to ensure that the tested models comparable and to prevent participants from collecting additional geospatial data. The dataset includes around 3 310,000 satellite input patches (13,000 reference areas x 12 months x 2 modalities) generating dataset of size approximately 300GB. The dataset is released as free and open-access under a CC-BY-4.0 license. To create a holdout test set, 30 % of the available patches from each year of 2018, 2020, and 2021, and 20 % of the available patches from 2019, were randomly selected. All patches from 2017 were placed in the training set due to the sparse Sentinel-2 time series from September 2016 August 2017 (when the second Sentinel-2B satellite had not yet launched) and consequently, the monthly images are potentially more cloudy.
Data Challenge Outcomes
The aim of the 'BioMassters' competition is to test different DI. methods on the benchmark dataset. The participants were asked to develop innovative DL methods for yearly AGB estimation. The competition lasted for three months (from 27/10/2023 to 27/01/2023); with 976 registrations around the globe and more than 1000 model submissions from the 90 active participants/teams. The final leaderboard highlighted that the top-10 models converge to an RMSE value (< 29 t/px), proving the high quality of the developed models. We choose and analyzed the top three models as baselines for the benchmark dataset. In the following subsection, we present the models and analyze their performance.
4.1 Baseline Models
The selected three baseline models are an adaption of U-TAE (U-Net Temporal Encoder)[[]) Swin U-Net TRansformer (Swin UNETR) 19 and U-Net++ [50]. The top
Attention
5

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three winning model implementations are available at the-bi biomassters
the model U-TAF 14 is a U-Net with temporal attention encoder. It takes a sequence of images and processes them using a shared convolutional encoder. An attention module is applied at the lowest resolution feature maps that generate a set of temporal attention masks for each pixel, which are then spatially interpolated at all resolutions. These masks are used collapse the temporal dimension of the feature map sequences into a single map per resolution. Similar to U-Net, a convolutional decoder is used that computes features at all resolution levels. All convolutions operate purely on the spatial and channel dimensions, and use strided convolutions for both spatial up and down-sammy down-sampling. Unlike the approach used in [14]. where the temporal attention masks are calculated at the lowest resolution and interpolated to mask higher resolution feature maps, the baseline model calculates and applies the attention mask separately at each resolution. The encoder used here is efficientnetv2 [40]. a state-of-the-ar backbone that is well-known for its strong performance on image classification tasks. The model takes in 15-band images from joint Sentinel-1 SAR and Sentinel-2 MSI data input, and the encoder is shared for all 12 months input data. For training, the reference labels are clipped between O and 400, The model uses data augmentation techniques such as vertical flips, rotations and random drops. In random drop, a random monthly image is dropped from the input sequence to avoid overfitting the sequence. The model is trained to optimize RMSE loss.
Model I:
Model 2: Swin UNETR [99 is a 3D semantic segmentation model and for AGB estimation, the 3D dimension used to incorporate time information. The model combines the U-Net architecture with Swin Transformer blocks. The encoder of the U-Net is replaced by Swin Transformer blocks, which have been shown to be effective capturing long-range dependencies and multi-scale features in images. The adapted Swin UNETR contains a four stage encoder to learn multi-scale features from input, and then a five stage decoder upsamples feature maps to the same spatial-temport size as input. Feature maps are averaged on the time channel and fed into the output head to generate segmentation prediction. The model input, reference labels and augmentation strategies are consistent with the U-TAE baseline model except random drop augmentations are not used here. Swin UNETR model is optimized using a weighted combination of mean absolute error and structure similarity loss (3).
The U-Net++ [50 architecture is an extension of the original U-Net model that was introduced for image segmentation tasks. Like the original U-Net, U-Net++ consists of an encoder and decoder that are connected through a series of skip connections. However, instead of using simple skip connections, U-Net++ uses series of nested dense convolutional blocks to bridge the semantic gap between the feature maps of the encoder and decoder prior to fusion. The third baseline employs an exhaustive ensemble of fifteen U-Net++ networks, each of which is implemented using U-Net++ with different backbone encoders, and each model is trained to optimize the Huber loss loss/51) The ensemble model is further trained with RMSE, and the final segmentation output is generated by taking the weighted average of the fifteen models. Unlike the previous models, this baseline explores preprocessing the training data. The Sentinel-1 and Sentinel-2 data are preprocessed to produce six cloud-free median composites to reduce data dimensionality while preserving the maximum amount of information. Different seasonal median composites are prepared for Sentinel-1 data and stacked. For Sentinel-2, first, cloud mask is applied with a 50% threshold of the cloud probability, and then the data is reduced to seasonal median composites and stacked. This baseline also explores different indices as input, such as NDVI for Sentinel-2 and VV/VH ratio for Sentinel- The data augmentation techniques used in this baseline are the same as those used in the other two baselines.
Model 3:
4.2 Results
All models are evaluated using RMSE evaluation metric between estimated and observed values which is calculated per-pixel for cach image and then averaged over all images. Few outliers (max 1-2 pixels in some chips) were observed in the reference AGB; they are not removed during the evaluation considering that are not statistically significant. Pixels with nonzero value are included in the assessment.
The test dataset is categorized into three classes low (0 to 68.56 t/px), medium (68.56 to 112.92 t/px) and high (>192 t/px) biomass density using 25 and 75 percentile values. The evaluation results on the three density categories and overall test dataset are shown in Table 2 The RMSE scores of the three baseline models are comparable, and they exhibit good performance in regions with lower biomass density. However, the biomass density increases, the accuracy of biomass

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estimation decreases, indicated by the increase in average RMSE. Out of the three models, U-TAE achieved the lowest RMSE score all four categories, making it the least biased model compared to others. The average bias (average per-pixel difference) values suggest that the U-TAE and swin UNETR models tend overestimate biomass, while the U-Net++ model slightly underestimated it. Additionally, the U-TAE model produced the least biased estimates in low and mid-density areas, while the U-Net++ model was least biased high-density areas. Figure we reported the
Table 2: Biomass estimation results from the three baseline models on the test set.
Method
Low Density
Medium Density
High Density
Overall
Average RMSE 1 Std.
U-TAE 14
15.24
1.4.99
28.55.19.80
37.59
27.491 12.14
Swin UNETR 19
15.25
14.41
28.61 19.85
37.64111.09
27.531
12.16
U-Net++ [50 Ensembled
13.60
14.35
29.01
38.041 10.93
27.921
12.11
Average Bias + Std.
U-TAE [14
0.054 + 15.84
0.37++30.22
1.76+:39.14
0.64+
30.04
Swin UNETR 19
0.43
+15.88
0.92+-30,25
2.37+-39.17
1.16+ 30.08
U-Net++ 50 Ensembled
-0.37 #16.19
-0.62.130.62
0.49139.55
-0.281 30.43
scatterplots for the three models computed on the test set, which shows the good correlation between reference label values and predictions and the trend of decreasing accuracy for higher densities. In future studies, it would be beneficial to identify the rest-performing models for different density areas and ensemble them improve the overall prediction accuracy. Figure 4 displays a few sample
R 0.765
R² 0.782
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101
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10
Juff vacca Label 2a
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250
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High
206
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Figure 3: Scatter plots of reference labels and predictions, (a), (b). (c) pixel-wise prediction comparisons and (d), (e). (f) are mean (per chip) prediction comparisons of U-TAE, Swin UNETR and U-Net++ ensemble. Scatter plots are prepared on the complete dataset. The dotted line represents the y=x axis and the solid line represents the line fitting the scatter plot. The verticle color labels represent the number of samples for each plot.
results from the three models. Similar to the average RMSE scores, the visualized results from the three models are comparable. However, a few distinguishing features are worth noting (highlighted in Figure 4.. For example, in the first sample (see highlighted area in the first row), swin UNETR and U-Net++ produced better outcomes. Upon closer inspection, we observed that the U-TAE and U-Net++ models generated somewhat blurre/ssmooth results, whereas the segmentation results from

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swin UNETR were fine seee-graind (see highlighted area in rows 2, and 4), resembling the reference label high-frequency details.
100
300
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750
200
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150
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370
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(a) Reference Label
(b) U-TAE
(c) Swin UNETR
(d) UNet++
Figure 4: Result samples from the three baseline models. The predictions from the three models quite similar. Some of the visual differences are highlighted white boxes.
Discussion
Over the past decade, several global maps of AGB have been produced such Baccini 2000, GeoCarbon, GlobBiomass and CCI Biomass. The AGB estimates are usually based on traditional These approaches used for AGB estimation are typically based on the physical interaction between SAR backscatter and canopy density. Although these models are widely excepted and have been extensively used for AGB estimation, they have some limitations: (1) it's more challenging to integrate multi-modal data (e.g. multispectral and SAR fusion) (2) they were developed for lower spatial and temporal resolution SAR images and are not exploiting the more recent satellite constellation capabilities. Furthermore, notable disparities exist in the worldwide biomass products, which limit their usefulness in climate and carbon cycle modeling, well in national assessments of forest carbon stocks and fluctuations [5]
remote sensing modeling approaches 38 39

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(a)
(b)
(c)
(d)
Figure 5: Visual comparison between (a) Google Earth high-resolution hase map (b) Reference ALS
AGB (c) Best Model Prediction (U-TAB) (d) ESA CCI Biomass 2018
Azara et al. S conducted a comprehensive assessment of available biomass products. They reported average RMSE values ranging from 30 t/px for areas with low biomass density 50 t/ha) 50 t/px for regions with higher biomass density (between 50 and 250 t/ha). Compared to average RMSE values obtained in their study, presented DL models gain a sigmificant improvement both low and high-density areas, with average RMSE values of 15 t/px and 37 t/px, respectively. Moreover, they reported that all global maps tend overestimate lower biomass and underestimate higher biomass. This phenomenon is common issue, also observed in the globally available biomass products (such as ESA CCI Biomass, and GeoCarbon). This is due saturation issues of MSI reflectance and SAR backscatter in densely forested areas. Our reported results also show decrease in AGB estimation accuracy with an increase in forest density (see Table 2). However, the use of multi-modal data helped in reducing the effect and showing more balanced behavior.
Another fundamental aspect consider when comparing the performance of the top models with respect to available biomass products is spatial resolution (pixel size in m). The existing products are generated at a coarse spatial resolution (e.g., 100m for ESA CCI Biomass), which ten times lower than the resolution achieved using our proposed BioMassters dataset. Figure 5 presents visual comparison between the reference LiDAR AGB (subfigure b)), the prediction of the best model (subfigure c) and the corresponding ESA CCI Biomass map (subfigure d). The ESA CCI Biomass product is generated using a combination Sentinel-1 and ALOS satellite imagery. From the visual comparison, it is clear that improvement in terms of spatial resolution is achievable using cutting-edge DL models.
However, the ESA CCI biomass is globally available and more studies should be conducted in the future to the scalability of the presented models. In particular, the dataset now limited only to boreal forests and it is not casy to transfer the model to other forest types, such as tropical forests. In the future, could be possible: (1) to extend the dataset to other regions where it's possible to obtain reliable airborne LiDAR data as reference; (2) to use the dataset for pre- pre-trained models and fine-tuned them to other regions or tasks We believe that fundamental aspect to improve biomass maps at the global scale is the release of more freely and open-access deep-leaming-ready datasets such as BioMassters.
6 Conclusion
In this study, we presented a benchmark dataset 'BioMasster' for forest AGB estimation. The idea to investigate the potential of dense time series multi-modal (Sentinel-1 SAR and Sentinel-2 MSI) satellite data. The dataset consists of 3 110,000 patches from a 5-year timespan that covers 8 million hectares of forested areas in Finland. The AGB references measurements conducted by the Finnish Forest Center and the National Land Survey (NLS) of Finland. The dataset has experimented with various DL models by participants from the "BioMasster" data challenge hosted by Driven Data. The top three baseline models achieved an RMSE of z 27 tonnes per 2,560 x 2,560 square meters area on an unseen holdout set. The results highlighted decrease in accuracy with the increase in forest density. The accuracy drop is mainly caused by a rise in the dispersion of the predicted biomass values; the systematic error component remains low showing a good balance (no over or underestimation) of the predictions for higher-density areas. We conducted
derived from airborne LiDAR

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visual comparison Figure 5 between predicted biomass maps and existing global AGB products to illustrate the potential of DL methods enhance spatial resolution. However, more studies are needed to verify the applicabilitty of the investigated models in different forest types (e.g. tropical regions) and the scalability from national to global scales. In summary, we have shown that the combination of multi-modal time series satellite data, specifically Sentinel-1 and Sentinel-2, with DL algorithms can provide a highly accurate estimation of AGB over vast areas, while also achieving impressive spatial resolution. Our findings suggest that this approach holds significant potential and the release of more freely accessible seep-learning-redde datasets and models would accelerate global-scale biomass mapping.
Acknowledgements
We would like to thank WathWorks for their generous contribution of prizes for the BioMasster data competitions (more information available at at/ttps://wwww drivendata, org/ competitions /9/ biomass- eereass-estiie cestimmaiin//paa page/534/) 534/) and express their gratitude to all the participants. This research work is also part of the EO-A14GIobaCChang project funded by Digital Futures.
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12


Reviews:
[{"review_id": "FpIjQwV8Mi", "reviewer": "Reviewer_fs8q", "summary": "", "questions": "", "limitations": "The current manuscript does not make limitations very clear. The paper describes the methods and performance quite clearly, but the paper may benefit from a more frank description of limitations. There may be limitations scattered throughout (eg in the conclusions it's mentioned that there's no tropical data), but a consolidated version would be helpful. ", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "There are many positive things about this paper. I will endeavor to highlight ones that I think may be missed by other reviewers that are not as familiar with non-Internet image data. My expertise though, is in other non-Internet data, so the AC and others should defer to someone who is a specific expert in remote sensing data. \n\n### S1 Data seems clean and machine-learning ready\n\nThe data seems cleanly processed. The steps make it clear that (a) basic but important-to-do-right processing has been applied to the data; this processing is, at least to me, non-obvious. The precise steps may seem small and potential \"obvious\" in retrospect, but in my experience, these are often difficult to know how to do. Having such a dataset that is machine learning ready is an obvious benefit to the community.\n\nAdditionally, various other things have been handled well: co-alignment (which is always a huge problem in these sorts of tasks); data splits; and selection of data such that temporal analysis is possible. \n\n### S2 The benchmark has already been run successfully\n\nOne challenge for contributions in the dataset/benchmark track is the question: is anybody going to actually use this? Often, a dataset is proposed and the reviewers and authors argue over whether the problem is of interest. The leaderboard is clearly active. 1000 submissions does include multiple people, but even a glance at the public leaderboard suggests that the benchmark has attracted substantial interest. I see this as a considerable plus. Obviously the prize for the initial run is a strong incentive, and so interest may drop off without a prize, but I can see this contribution as: (a) providing a benchmark going forward (potentially for researchers who are motivated to specifically solve this problem); (b) providing a historical test of what was feasible by well-incentivized researchers.\n\n### S3 The analysis is pretty thorough\n\nThe paper explains the data clearly and does good analysis of the results. Results include\n- error metrics (i.e., how big is the error)\n- bias (i.e., does the predicted data skew one way or another)\n- scatterplots of the results at both a pixel level and tile level. \n- qualitative results\n\nThese results make the results of what's feasible clear (although I do have some clarity suggestions). ", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "xGMbRoS8D8", "reviewer": "Reviewer_7vh3", "summary": "", "questions": "", "limitations": "While the performance of the baseline models is compared, and is it mentioned, that more studies on the proposed models are needed, the authors don’t discuss general limitations of the benchmark, such as for example the global availability of ground truth data (in the study, forest inventory data from the Finland Forest Centre is being used which is available under an open license). \nThe authors don’t discuss potential societal impacts of their work.\n", "rating": 7, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "The submission provides a benchmark for forest biomass, comprising a multi-modular benchmark dataset and three baseline models architectures. Its contribution to the research community is significant as of the increasing interest and urgency for tools for biomass quantification at scale.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "8O1RkuQA4I", "reviewer": "Reviewer_ZjmW", "summary": "", "questions": "", "limitations": "Limitations are not discussed in the paper. However, the expected societal implications of this work are discussed in the Introduction", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "- Hosting the competition on DrivenData means that the dataset is well documented and accessible (as others have already leveraged it). In addition, it means that the winning repositories are public, and can be built on\n- In addition, the dataset and metrics are thoughtfully constructed so that this can be a meaningful benchmark for biomass estimation in the future", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "rW5P7R57Xf", "reviewer": "Reviewer_bUNV", "summary": "", "questions": "", "limitations": "N/A", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "- the task is important and original\n\n- the dataset is valuable and well-designed\n\n- the contest has been successful, with many submissions", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "6TdArUhxuE", "reviewer": "Reviewer_dBnr", "summary": "", "questions": "", "limitations": "See \"summary and contributions\".", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "See \"summary and contributions\".", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}]

Author rebuttal:

Meta-review: The submission introduces a competition and benchmark dataset for (above-ground) forest biomass estimation based on Sentinel-1 (SAR) and Sentinel-2 (Multispectral) inputs. The groundtruth reference is given as a combination of forest plots and airborne LiDAR data. Given the well-prepared dataset, available benchmark models, the active community around it, and the detailed evaluation protocol, this submission does provide a good foundation for further research in this domain.

Based on the reviewers' feedback and overall rating, the decision is to recommend the paper for acceptance. We would ask the authors to work through all recommended improvements as given by the reviewers for the final version of the paper.
Decision: Accept (Poster)