CarbonSense: A Multimodal Dataset and Baseline for Carbon Flux Modelling
Authors: Matthew Fortier
Mats Leon Richter
Oliver Sonnentag
Christopher Pal
Venue: ICLR 2025 Poster (2025)
Keywords: dataset
machine learning
deep learning
multimodal models
climate chage
carbon dynamics
benchmark
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: We provide a dataset and benchmark for data-driven carbon flux modelling, a vital component of monitoring ecosystem health.

Abstract: Terrestrial carbon fluxes provide vital information about our biosphere's health and its capacity to absorb anthropogenic CO$_2$
 emissions. The importance of predicting carbon fluxes has led to the emerging field of data-driven carbon flux modelling (DDCFM), which uses statistical techniques to predict carbon fluxes from biophysical data. However, the field lacks a standardized dataset to promote comparisons between models. To address this gap, we present CarbonSense, the first machine learning-ready dataset for DDCFM. CarbonSense integrates measured carbon fluxes, meteorological predictors, and satellite imagery from 385 locations across the globe, offering comprehensive coverage and facilitating robust model training. Additionally, we provide a baseline model using a current state-of-the-art DDCFM approach and a novel transformer based model. Our experiments illustrate the potential gains that multimodal deep learning techniques can bring to this domain. By providing these resources, we aim to lower the barrier to entry for other deep learning researchers to develop new models and drive new advances in carbon flux modelling.

Published as a conference paper at ICLR 2025
CARBONSENSE: A MULTIMODAL DATASET AND BASE- LINE FOR CARBON FLUX MODELLING
Matthew Fortier
Mats L. Richter
Mila Quebec AI Institute ma matthew. fort ientieermiii la quebec
ServiceNow Research
Oliver Sonnentag
Chris Pal
Département de géographie Université de Montréal
Mila Quebec Al Institute Polytechnique Montréal Canada CIFAR AI Chair
ABSTRACT
Terrestrial carbon fluxes provide vital information about our biosphere's 's health and its capacity to absorb anthropogenic CO2 emissions. 'he importance of predicting carbon fluxes has led to the emerging field of data-driven carbon flux modelling (DDCFM), which uses statistical techniques to predict carbon fluxes from biophysical data. However, the field lacks a standardized dataset to promote comparisons between models. To address this gap, we present CarbonSense, the first machine learming-reat ready dataset for DDCFM. CarbonSense integrates measured carbon fluxes, meteorological predictors, and satellite imagery from 385 locations across the globe, offering comprehensive coverage and facilitating robust model training. Additionally, we provide baseline model using current state-of- art DDCFM approach and a novel transformer based model. Our experiments illustrate the potential gains that multimodal deep learning techniques can bring this domain. By providing these resources, we aim to lower the barrier to entry for other deep learning researchers to develop new models and drive new advances carbon flux modelling.
INTRODUCTION
The biosphere plays a critical role in regulating Earth's climate. Since the mid-20th century, terrestrial ecosystems have absorbed up to third of anthropogenic carbon emissions ( However, climate change introduces uncertainty about the future resilience and capacity of these ecosystems. Under- standing how the carbon dynamics of our biosphere are changing response to both climate change and increasing anthropogenic pressures will give crucial insight into the health of our ecosystems and their ability sequester carbon in the future.
Carbon fluxes describe the movement of carbon into and out of these ecosystems resulting from processes like photosynthesis and cellular respiration. They play a key role in assessing an ecosystem's health, but measuring them requires long-term field sensor deployment to cover an area of only 100- 1000m² (2) This bottleneck has given rise the field of data-driven carbon flux modelling (DDCFM) where researchers use biophysical predictors such as meteorological and geospatial data to model carbon fluxes. By training on data from a variety of field sites in varying ecosystems, these models can be used to predict carbon fluxes regionally globally 3
DDCFM presents fascinating topic for deep learning researchers with real-world impact, yet remains underexplored. As result, the current state-of-the-arr (SOTA) uses off-the-shelf solutions like random forests (577). gradient boosting [4). or ensembles of similar methods [8] []. These methods produce satisfactory results, but they fail to capitalize on the multimodal nature of the biophysical data. Recently, multimodal deep learning has exploded in popularity (10, 11] and may offer appropriate framework for DDCFM through effective data integration and advanced neural architectures. Such advances could significantly enhance the quality of information available to decision-male makers,,, thereby improving ability address climate change.

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We wish to lower the barriers to entry into this and encourage work DDCFM from the deep learning community. Data preparation for DDCFM is currently performed ad-hoc by research teams, leading inconsistency and lack of standardization. The absence of standardized datasets and benchmarks hinders reproducibility and comparability of research findings. Our work addresses these gaps with the following contributions:
We provide an overview of DDCFM for deep learning researchers (Section
We publish multimodal machine learning-reat -rand (ML-ready) dataset for DDCFM with over 20 million hourly observations from 385 sites (Section
We provide a baseline model based on current SOTA practices in DDCFM, and compare with multimodal deep learning model which achieves improved performance (Section 3
We will discuss our experiments in section and provide guidelines for reporting results in this domain.
2
CARBON FLUX MODELLING
The application of machine learning techniques to model carbon fluxes was present as early as 2003 where researchers used early neural networks for regional flux upscaling. Global upscaling models were first seen in 201 1 [13], facilitated by the global research network FLUXNET which pooled carbon flux data from research teams around the world. Subsequent releases FLUXNET increased the data quality and number of sites [14). Recent models such those developed by FLUXCOM 3 4 use a combination of meteorological data and geospatial data to improve model conditioning. Targeted modelling of specific regions such subtropical wetlands (9 arctic and boreal areas [7) allow researchers to develop a better understanding of the carbon cycle at these sites without concern for global generalization.
At its core, DDCFM is a regression problem. The target (carbon flux) depends on many factors including ecosystem composition, meteorological conditions, local topography and geology, and disturbances (fires, animal activity, etc). Meteorological data is relatively easy to obtain, but the other predictors are challenging measure and represent, especially at global scale. Remote sensing and semantic data are commonly employed as a proxy for the other predictors.
2.1 MEASURING FLUXES
The most common technique for measuring fluxes at ecosystem scale is eddy covariance (EC) [15]. This micrometeorological tech- nique where researchers erect tower (typically above canopy height) and mount sensors that measure atmospheric gas concentrations across small turbulent vortices (eddies). A simplified EC station is depicted in Figure [ CO2 and water vapour are the most widely measured, but some towers also measure methane (CH,) [5] or nitrous oxide (N20) [16]. Our work focuses on CO2 due the prevalance of standardized data collections.
Wind
Figure 1: Simplified EC station. Sensors measure
atmospheric gas concentrations across eddies.
Carbon fluxes are expressed as mass area time (ex 9 m -2. hr-1), Gross primary productivity (GPP) refers to the total carbon uptake by plants through photosynthesis. Ecosystem respiration (RECO) is the total carbon returned to the atmosphere through both plant and microbial respiration. Net ecosystem exchange (NEE) is the small difference between the two large component fluxes, GPP and RECO; a carbon sink will have a negative NEE as more carbon is being consumed through GPP than released through RECO. NEE is the flux that is directly measured by EC stations and is the main focus our experiments, but GPP and RECO (which are derived from NEE) are also provided in our dataset.
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2.2 FLUX PREDICTORS
Meteorological Data DDCFM meteorological data comes from EC stations. In addition to carbon fluxes, EC stations measure local environmental and atmospheric conditions such as radiation, air temperature and relative humidity, precipitation, soil moisture and temperature, etc. The exact number and type of variables depends on the site, but regional networks maintain minimum mandatory set for researchers wishing submit their data [14]. For trained models looking to predict fluxes the global scale, meteorological data can be obtained from publicly available reanalysis products such as ERAS 17 which provides the variables on 0.25-0.5 degree grid.
Geospatial Data Satellite imagery of the area surrounding an EC station can give useful information about the land cover and ecosystem makeup. The most common products for DDCFM are based on Moderate Resolution Imaging Spectroradiometer (MODIS) data [18). This satellite pair ("Aqua" and "Terra") produce new imagery for Earth's 's surface every 1-2 days and have 36 spectral bands with resolutions varying between 250m and kkm. The MCD43A4 product is particularly common fuses MODIS data in a 16-day sliding window to produce single image each day, This helps to address cloud coverage and produces images which remove angle effects from directional reflectance [19.. Each image therefore appears as would from directly overhead solar noon. MCD43A2 also widely used, and contains categorical values for each pixel indicating snow and water cover (20). The terms "geospatial data", "satellite data" and "remote sensing data" are often used interchangeably in this domain, but it should be noted that not all geospatial data comes from satellites.
Semantic Data Some models ingest semantic data such as land cover ("Croplands", "Evergreen needleleaf forest", "Snow and ice", etc). Land cover classifications follow standardized schemes such as the International by domain experts, but some MODIS products coarsely approximate this information on global grid [2I), allowing this data to also be used for global inference.
Seesphere-Biosphee
Programme (IGBP). Land cover classification performed
THE CARBONSENSE DATASET
We present the first MI-rredy dataset for DDCFM, CarbonSense. CarbonSense consists of EC station data and corresponding MODIS geospatial data for 385 sites across the globe, totalling over 27 million hourly observations. This section provides a brief overview of the dataset structure, processing pipeline, and usage guidelines. A more comprehensive guide is given in the supplementary material. For detailed list of the 385 locations and their respective ecosystem types, see Appendix A
3.1 DATA COLLECTION
meteorological data was aggregated from major EC data networks, including FLUXNET 2015 [14]. the Integrated Carbon Observation System (ICOS) 2023 release (22). ICOS Warm Winter release [23], and Ameriflux 2023 release
[24]. These source datasets were chosen due to their use of the ONEFlux processing pipeline (14). ensuring standardized coding and units. A map of EC sites and their source networks shown in Figure North America and Europe are over-represented in this site list due greater data accessibility, and we discuss the implications of this in Section 3.3
Geospatial data in CarbonSense are sourced from MODIS products. Specifically, we utilize the seven spectral bands from the MCD43A4 product [19). as well as the water and snow cover bands from MCD43A2 20). Following the guidelines from (8). we extract images in 4km by 4km square centered on each EC station. Given spatial resolution of 500m per pixel, this yields an 8x8 pixel image with 9 channels for every site-day.
3.2
DATA PIPELINE
The first stage in the pipeline is EC data fusion. Many sites had overlapping data from different source networks. For example, the site Degero in Sweden (SE-Deg) had data from 2001-2020 in the ICOS Warm Winter release, and data from 2019-2022 in the ICOS 2023 release. Data were fused with overlapping values taken from the more recent release in previous DDCFM work (4) Any
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25
0-
-25
Network Source
Ameriflux
-50
FLUXNET
ICOS 2023
ICDS Warm Winter
Mu Multiple
-150
1000
-50
50
100
150
Figure 2: Global map of eddy covariance sites used in CarbonSense, with corresponding source networks. Some sites were present in multiple networks.
sites which report half-hourly data were downsampled to hourly at this stage, and daily and monthly recordings were discarded.
Once fused, extracted the relevant time blocks for each EC station along with its geographic location. This metadata was used to obtain the appropriate MODIS data for each site. Data was pulled procedurally from Google Earth Engine [25]
Meteorological data was pruned to remove unwanted variables. Some, like soil moisture and temperature, were either unavailable for most sites were heavily gap-filled. We removed these variables to reduce the risk of compounding errors on the underlying pipeline gapfilling techniques. full list of variables this stage given Table 6
As final stage in the pipeline, we apply min-max normalization on predictor variables. We map cyclic variables (those with a cyclic range such as wind direction) range [-1, 1) and acyclic variables to the range [-0.5, 0.5). This normalization procedure is conducive to our Fourier encoding method discussed in Section
We offer CarbonSense as finished dataset but also provide the raw data. The full pipeline code is available so that researchers can run and modify it freely. Our pipeline can be configured to include other variables or to have different "leniency" for gap-filled values. For example, those who wish to use CarbonSense with strictly observed values may do so the cost a smaller number of samples. A diagram of the entire pipeline is shown in Figure
Variable Selection
FLUXNET
Data Fusion
Aggregated Metecrological Data
Normalization
CarbonSense (Unnormalized)
CarbonSense (Normalized)
Ameriflux
ICOS 2023
ICOS WW
Metadata Extraction
Cropping. Reprojection
Eddy Covariance Source Networks
EC Station Metadata
MODIS Data
(Raw)
Retrieval (Earth Engine)
Figure 3: Data pipeline used create CarbonSense from EC and MODIS data.
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3.3 USING THE DATASET
Site Sampling The biased geographic and ecological distribution of sites remains a challenge in DDCFM, and CarbonSense is different. Given the significant overrepresentation of certain regions (North America, Europe) and ecosystems (evergreen needleleaf forests, grasslands), we maintain partitioned structure where each site its own directory containing EC data, geospatial data, and metadata. Researchers encouraged to select sites for training and testing based on their experiment objectives such as high performance on particular ecosystems, or out-of-diitributiin generalization. Our experiments in section 5 are example of the latter.
Dataloader We supply example Py'Torch dataloader for CarbonSense specifically tailored to our model. Using the dataloader requires specifying which carbon flux to use as the target (ex NEE, GPP, RECO), which sites include in each dataloader instance, and the context window length for multi-timestep training.
Licensing CarbonSense is available under the CC-BY-4.0 license, meaning it can be shared, transformed, and used for any purpose given proper attribution. This an extension of the same license for all three source networks, and MODIS data is provided under public domain. We feel that permissive licensing essential order foster greater scientific interest in DDCFM.
4 THE ECOPERCEIVER ARCHITECTURE
In this section we present EcoPerceiver, a multimodal architecture for DDCFM. The SOTA for DDCFM are tabular methods, and we felt it would be appropriate to include a baseline model which demonstrates how deep learning concepts can be leveraged for this unique problem domain.
EcoPerceiver based on the Perceiver architecture [26), which cross attends a variable number of inputs onto compact latent space, allowing for extreme input flexibility. Missing inputs common in DDCFM due to coverage gaps, outlier values, or failing sensors. Rather than rely on gapfilling techniques, chose this architecture for its robustness to missing inputs.
We also wanted model which could ingest data from a varying time window. To our knowledge, this is the first DDCFM model to treat carbon dynamics non-Markovian with respect predictors. We feel this more accurately reflects biological processes, since a plant's rate of photosynthesis may also depend conditions hours days into the past. Our ablation experiments in Appendix B.7 explore this idea further.
EcoPerceiver
Modality Mask (T.V)
Tabular Input (T.ve)
Fourier Encoding
WCA
CSA
Linear
Logits
Remate Sensing Data (Veriable)
Spectral Encoding
xN
xM
Arrays
Figure 4: Overview of EcoPerceiver architecture.
4.1 DATA INGESTION
Small fluctuations in meteorological variables have the potential influence ecological processes. For this reason, it is important that the model is sensitive to small changes in input values. We take inspiration from NeRF's Fourier encoding 277 which maps continuous values to higher dimensional
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space with high frequency sinusoids. Each variable x is therefore encoded as:
[z; K) - sin(2" (T)), cos(2"
A E [0, [0,
where K' is a hyperparameter indicating the maximum sampling frequency. Higher values of K allow the model to better discern between small differences input. With our normalization scheme, cyclic variables at values of -1 and 1 will produce identical vectors under this transform as intended. Each input is given a learned embedding specific to the underlying variable. This then concatenated with the Fourier encoding to produce final input vector of length H, 2K lemb for each input. Figure 5 depicts this encoding procedure.
Geospatial data is similarly processed, except that each spectral band is flattened and mapped into vector of length 2K via linear transforma- tion instead of Fourier encoding. Each band is pro-
Fourier Encoding (per tabular value)
then concatenated with an embedding duce a vector of length H;. We then stack the encoded data to create a matrix of shape (T. Ve, H₂) where V2 is the total number of vari- ables (tabular values spectral bands) and 7 is the context window length.
Variable (eg. temp)
concat
Encoded Variable (H)
embedding
To account for missing values and timesteps without geospatial data, EcoPerceiver takes modality mask indicating which values to ig- nore in the cross attentive layers. This modality mask doubles as a dropout mechanism which small subset of vari-
Figure 5: Fourier input encoding for EcoPerceiver. Spectral inputs are similarly processed, but with linear projection instead of Fourier encoding.
reduces over-reliance ables (observational dropout).
4.2 WINDOWED CROSS ATTENTION
We build Perceiver's s core concept of cross-attending data onto compact latent space for process- ing. EcoPerceiver uses a latent space of size (T, Hi) where Hi is the latent hidden dimension. Each token extracts input data via cross-attention from its respective timestep's observations. Intuitively, each token may represent the ecosystem's "state" at a particular time, and ingests observations from that timestep.
1 This operation would be inefficient with vanilla cross attention, each token would use at most T observations with an attention mask removing the rest. We take inspiration from SWin Transformer (28) and instead push the context window dimension (T) into the batch dimension for both input and latent space. The resulting Windowed Cross Attention (WCA) has runtime of O(T V₁ Ho) where Ha is the projection dimension.
keeping with Perceiver, each WCA operation is followed by self-attention operation the latent space. We pass causal mask to the self attention so each timestep is conditioned only on past and present observations. We refer to this as Causal Self Attention (CSA). This constitutes a full WCA block shown in Figure
WCA blocks are repeated N times, repeatedly cross attending inputs onto the latent space with self attention in between. We then apply series of M CSA operations and use the final timestep's 's token as input to a linear layer. The output of this is the estimate the desired carbon flux.
5 EXPERIMENTS
In this section, we present a series of experiments using CarbonSense, Our primary analysis includes two models: an EcoPerceiver model as introduced in 4 and XGBoost model (29) implemented to mimic current SOTA approaches in DDCFM 44. We demonstrate the power of tailored deep architectures for DDCFM and establish robust baseline that will support and inspire future research efforts. We also present guidelines for rumning similar experiments and presenting results. Further model comparisons are explored in Appendix alongside ablation studies and qualitative analysis.

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Modality
Mask (T.Vi)
Windowed Cross Attention Block
Causal Attention Mask
Encoded Input
(T.V).H)
Windowed
Latent Array (T.L.M))
Cross Attention
Self Attention
Figure 6: Windowed Cross Attention (WCA) block. Encoded inputs cross-attended onto the latent space with modality mask to indicate missing values. The time dimension is pushed into the batch dimension, this operation is performed B 7 times per batch. Causal self attention proceeds as normal.
5.1 DATA SPLITTING
ECC stations were randomly divided into train and test sets based on their IGBP ecosystem classification (IGBP type). We mostly refer to IGBP types by their acronyms for brevity, but list of IGBP types with expanded names is found in Appendix A
Despite the imbalance of IGBP types in CarbonSense, wanted the test set to be as balanced as possible. The number of sites in the test set were determined with min(5, [0.2 num sites]). This provided between and 5 sites per IGBP type shown in Table [ As a consequence, SNO and DNF provide information about zero-shot generalization, and CVM and WAT about one-shot generalization.
Table I: Train / test split distri-
bution by IGBP type
IGBP
Train
Test
WET
42
DNF
0
The main focus of this research is on models trained across different ecosystem types, as opposed to other research studying DDCFM within a single type (ex 8 9) However, the partitioned nature of CarbonSense makes it flexible for different modelling objectives, such as individual ecosystems. We give an example of this in Ap-
WSA
8
EBF
10
ENF
DBF
42
CRO
44
pendix B.8
MF
10
GRA
59
OSH
25
CVM
5.2 MODEL CONFIGURATIONS
CSH
5
SAV
11
SNO
0
EcoPerceiver experiments were each run on A100 GPUs using dataset parallelization. The train sites were further divided into train and validation splits at a 0.8 0.2 ratio respectively. We used the AdamW optimizer [30] with learning rate of 8e-5 and a batch size of 4096. A single warm-up epoch was performed followed by a cosine annealing learning rate schedule over 20 epochs, but all experiments converged between 6 and 13 epochs.
WAT
1
ooBoost experiments were run on CPU nodes. We designed our XGBoost experiments to resemble [4] closely as possible. This allows to compare EcoPerceiver's 's relative performance against stand-in for the SOTA. Appendix provides a detailed description of XGBoost data preprocessing, hyperparameters for all models, ablation studies, and more.

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5.3 METRICS
The most commonly used performance metric in DDCFM (and any form of hydrologic modelling) is the Nash-Sutcliffe Modelling Efficiency (NSE) [T]. described with the following equation:
NSE(x) = Σ, (1 x;)
(2)
E,( 5)²
where value of represents perfect correlation between and y. value of represents the same performance as guessing the mean of and negative values indicate that the mean of y is better predictor than æ. NSE is challenging dataloader to also provide the mean of the data for a given site or ecosystem type. We therefore use mean squared error (MSE) as a loss function and report its root (RMSE) as well as NSE in our results. Data balance in results reporting is also concern. At first glance, the data appears very imbalanced with respect to ecosystem prevalence. CarbonSense contains 64 grasslands (GRA) sites, but only deciduous needleleaf forest (DNF). While this is extreme gap, ecosystems are more diverse than IGBP types can capture; grasslands central North America will differ significantly from those in Europe or Asia. Still, it is prudent to separate results by IGBP type give better picture of model performance.
directly
loss function since would require the
5.4 RESULTS
We ran 10 experiments with each model using different seeds get an accurate picture of performance. Tablee[shond the mean performance on the test set for each model over every IGBP type, and we give example of model output visualization in Figure
Table 2: NSE and RMSE by model and IGBP type, aggregate mean across 10 seeds. Bold numbers indicate better performance. Pairwise t-test results are given for NSE values with degrees of freedom.
XGBoost
EcoPerceiver
t-test (NSE)
IGBP
NSE
RMSE
NSE
RMSE
I-statistic
p-value
CRO
0.8066
3.2381
0.8482
2.8677
13.4689
0.0000
CSH
0.7510
1.5224
0.7670
1.4709
1.9947
0.0772
CVM
0.5277
5.5157
0.5763
5.2236
9.6586
0.0000
DBF
0.7250
4.0959
0.7547
3.8678
10.5993
0.0000
DNF
0.2803
4.0974
0.4336
3.6322
8.6338
0.0000
EBF
0.7966
4.6050
0.8220
4.3070
8.1990
0.0000
ENF
0.7765
2.8141
0.7694
2,8579
-2.3853
0.0409
GRA
0.7461
3.2487
0.7967
2.9059
13.7609
0.0000
MF
0.7559
3.8633
0.7717
3.7361
8,3540
0.0000
OSH
0.5451
1.8796
0.6060
1.7475
3,9356
0.0034
SAV
0.5802
1.6514
0.7368
1.3070
28.0814
0.0000
SNO
-0.0370
1.4291
0.2898
1.1816
16.3974
0.0000
WAT
-11.0524
3.1838
-14.4010
3.5802
-2.4809
0.0349
WET 0.4530
2.2073
0.4137
2.2830
-2.1005
0.0651
WSA
0.6132
2.5153
0.6267
2.4706
2.6798
0.0252
EcoPerceiver outperformed the XGBoost baseline across most IGBP types. XGBoost performed better in permanent wetlands (WET), water bodies (WAT), and evergreen needleleaf forests (ENF) by slim margin. These are the three key ecosystems of the boreal biome, indicating XGBoost retains an advantage in that region. WAT is particularly far out of distribution (EC stations are mounted above lakes) and both models did worse than predicting the mean, indicating this could be an issue with data quantity.
Besides WAT, EcoPerceiver did substantially better on zero- and one-shot tests. High predictive power uut-of-distribuiin sites like this is especially important for researchers wishing to run inference on global data, where each grid cell is likely to be quite different from any the training sites.
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Our results also underline the importance of using NSE as the main metric for evaluation. Consider the models' performance on open savannas (SAV). XGBoost had an RMSE of 1.6514 versus EcoPer- ceiver's 1.3070. The magnitude of difference is small, and both values significantly lower than the RMSE of many other IGBP types. But XGBoost had an NSE of 0,5802 while EcoPerceiver achieved 0.7368 which is significant improvement. Different ecosystems have different variances in their carbon fluxes, and NSE accounts for this by dividing the performance by the variance of the target.
AU-ASM: 2012-06-09 2012-06-11
Model
XGBeost
EcoPerceiiver
Truth
2012-06-10
2012-06-11
Figure 7: Example NEE measurements and model predictions from an Australian cropland site. Qualitative analysis of model outputs is important in DDCFM, and is discussed in Appendix B.9
CONCLUSION
Our work establishes foothold for deep learning in the field of DDCFM. We provide an open source ML-reedy dataset, CarbonSense, using EC station data and geospatial data from variety of ecosystems. DDCFM is inherently a multimodal task, and our baseline model EcoPerceiver demonstrates that recent advances in multimodal deep learning can unlock substantial performance gains in this domain. We implore more deep learning researchers to help develop this field further, because the potential of artificial intelligence to improve world can only be realized if we actively apply it to solve pressing social and environmental issues.
Future Work Our work leaves much be explored in both dataset and model development. CarbonSense can be expanded as more EC station data is incorporated into regional network releases. Additional geospatial data could be added in the form global soil products or higher resolution satellite imagery. Future models may work to address the shortcomings of EcoPerceiver in the boreal ecosystems, or incorporate more sophisticated fusion techniques like the use of convolutional layers in image ingestion.
Limitations Data diversity remains the biggest challenge in this domain. CarbonSense has data imbalance in only ecosystem types, but geographic location. Africa, Central Asia, and South America are all underrepresented. While these areas contain many EC stations, most do not have readily available data in ONEFlux format, presenting barrier to their inclusion in CarbonSense. Researchers should be aware of the consequences of developing models with imbalanced data, including poor performance in underrepresented areas.

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[113] J. D. Forsythe, M. A. Kline, and T. L. O'Halloran, "AmeriFlux FLUXNET-IE IF US HB2 Hobcaw Barony Mature Longleaf Pine, Ver. 3-5," AmeriFlux AMP, 2023. DOE: http
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[135]
National Ecological Observatory Network (NEON), "AmeriFlux FLUXNET-I IF US-xSB
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[158] S. Ma, L. Xu, J. Verfaillie, and D. Baldocchi, "AmeriFlux FLUXNET-IF US- Var Vaira Ranch- Ione, Ver. 3-5," AmeriFlux AMP, 2023. DOI: http:: org 17190/ AME
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[177] E. Euskirchen, G. Shaver, and S. Bret-Harte, "AmeriFlux FLUXNET-IE IF US Imnavait Creek Watershed Tussock Tundra, Ver. 3-5," AmeriFlux AMP, 2022. DOE: htts:
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[200] T. Papakyriakou, M'meriiFlxx CA-CFI Churchill Fen Site 1, Ver. 3-5," Ameri-
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Appendices
A EDDY COVARIANCE SITE DETAILS
Here we provide an exhaustive list of EC sites used in CarbonSense along with their most recent publication. As per Ameriflux's 's data policy, each site has an individual citation with DOI; other networks simply required citation of the unified release. It would be impractical to have each site s full description in these tables, but the first two letters of each code represent the country where the site is located (ex. "DE" for Germany).
We also enumerate all meteorological predictors and targets in Table 6 and provide a temporal distribution of EC data by year in Figure 8
Table 3: EC Sites
Croplands (CRO)
BE-Lon
22
CA-ERI
32
CA-MAI
33
CA-MA2
34
CH-Oe2
23
CZ-KrP
23
DE-Geb
22
DE-Kli
DB-RuS
22
DB-Seh
4
DK-Fou
14
DK-Vng
22
FI-Jok
FI-Qvd
23
FR-Aur
22
FR-EM2
22
FR-Gri
22
FR-Lam
22
IT-BCi
23
IT-CA2
14
US-A74
35
US-ARM
36
US-Bil
37
US-Bi2
38
US-CF1
39
US-CF2
40
US-CF3
41
US-CF4
42
US-CRT
43
US-CS1
US-CS3
45
US-CS4
|6
US-DFC
47
US-DS3
48
US-Lin
49
US-Mol
50
US-Mo3
5T
US-Ne1
52
US-RGA
33
US-RGB
54
US-RGo
55
US-Ro1
56
US-Ro2
US-Ro5
558
US-Ro6
59
US-Tw2
60
US-Tw3
US-Twt
US-XSL 62
Closed Shrublands (CSH)
BE-Maa 22
IT-Noe [4]
US-KS2 63
US-RIs (64]
US-Rms (65)
US-Rwe [66
US-Rwf
Cropland/Natural Vegetation Mosaies (CVM)
US-HWE 68
US-xDS 669
Deciduous Broadleaf Forests (DBF)
AU-Lox
14
BE-Ler
22
CA-Cbo
70
CA-Oas
14
CA-TPD
71
CZ-Lnz
22
CZ-Stn
23
DE-Hai
22
DE-Hzd
23
DE-Lnf
14
DK-Sor
22
FR-Fon
22
FR-Hes
22
IT-BFt
22
IT-CA1
14
IT-CA3
IT-Col
IT-Isp
IT-PT1
14
IT-Rol
14
IT-Ro2
14
JP-MBF
14
MX-Tes
72
PA-SPn
14
US-Bar
73
US-Hal
US-MMS
75
US-MOz
76
US-Oho
77
US-Rpf
78
US-UMB
79
US-UMd
80
US-WCr
14
US-Wil
8T
US-Wi3
82
US-Wi8
83
US-xBL
US-xBR
85
US-xGR
US-xHA
87
US-xML
88
US-xSC
89
US-xSE 90
US-xST
91
US-xTR
922
US-xUK
93
ZM-Mon
Deciduous Needleleaf Forests (DNF)
BR-CST [94]
Evergreen Broadleaf Forests (EBF)
AU-Cum
14
AU-Rob 14
AU-Wac 14
AU-Whr L4
AU-Wom 14]
BR-Sa3
14
CN-Din
FR-Pue 22
GF-Guy 22
GH-Ank
14
IT-Cp2 22
IT- IT-Cpz
MY-PSO
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Table 4: EC Sites (cont'd)
Evergreen Needleleaf Forests (ENF)
AR-Vir
14
CA-Cal
95
CA-Ca2
96
CA-LPI
97
CA-Man
CA-NSI
98
CA-NS2
99
CA-NS3
100
CA-NS4
101
CA-NS5
I02
CA-Obs
14
CA-Qfo
103
CA-SF1
104
CA-SF2
105
CA-TPI
106
CA-TP2
14
CA-TP3
107
CA-TP4
14
CH-Dav
22
CN-Qia
14
CZ-BKI
22
CZ-RAJ
25
DE-Lkb
14
DE-Msr
22
DE-Obe
23
DE-RuW
22
DE-Tha
22
DK-Gds
22
FI-Hyy
22
FI-Ken
22
FI-Let 22
FI-Sod
14
FI-Var
22
FR-Bil
22
FR-FBn
23
FR-LBr
14
Lat
23
IT-Laa
14
IT-Lav
23
IT-Ren
22
IT-SR2
22
IT-SRo
14
NL-Loo
RU-Fy2
23
RU-Fyo
SE-Htm
SE-Nor
22
SE-Ros
23
SE-Svb 22
US-BZS
108
US-Blo
US-CS2 109
US-Fmf
110
US-Fuf
US-GBT
US-GLE
112
US-HBB
US-HB3
US-Ho2
US-KS1
US-Mel
US-Me2
T18
US-Me3
US-Me4
US-Me5
14
US-Me6
T20
US-NCI
US-NC3
122
US-NRI
US-Pr
US-Vem
US-Vep
T25
US-Wi0
26
US-Wi2
US-Wi4
US-Wi5
US-Wi9
129
US-xAB
130
US-xBN
13
US-xDJ
132
US-xJE
135
US-xRM
134
US-xSB
135
US-xTA
136
US-xYE
137
Grasslands (GRA)
AT-Neu
14
AU-DaP
14
AU-Emr
14
AU-Rig
14
AU-Stp
14
AU-TTE
14
AU-Yne
14
BE-Dor
23
CA-MA3
138
CH-Aws
23
CH-Cha
23
CH-Fru
23
CH-Oel
14
CN-Cng
14
CN-Dan
14
CN-Du2
14
CN-Du3
14
CN-HaM
CN-Sw2
CZ-BK2
14
DE-Gri
22
DE-RuR
22
DK-Eng
14
FR-Mej
FR-Tou
22
GL-ZaH
22
IT-MBo
23
IT-Niv
22
IT-Tor
22
NL-Hor
PA-SPs
14
RU-Hal
14
SE-Deg
22
US-A32
139
US-ARI
140
US-AR2
14
US-ARb
US-ARc
US-BRG
US-Cop
145
US-Goo
US-Hn2
146
US-IB2
14
US-KFS
147
US-KLS
148
US-Kon
149
US-Mo2
150
US-NGC
15
US-ONA
152
US-Ro4
US-SRG
154
US-Seg
155
US-Sne
156
US-Snf
157
US- Var
158
US-Wkg
159
US-xAE
T60)
US-xCL
161
US-xCP
162
US-xDC
163
US-XKA
164
US-xKZ
[65
US-xNG
166
US-xWD
167
Mixed Forests (MF)
AR-SLu
14
BE-Bra 22
BE-Vie 22
CA-Gro 168
CD-Ygb 22
CH-Lac 23
CN-Cha
14
DE-Har 22
DB-HoH 22
JP-SMF 14
US-Syv 169
US-xDL
[170]
US-XUN
Open Shrublands (OSH)
CA-NS6
172
CA-NS7
24
CA-SF3
14
ES-Agu
23
ES-Amo
14
ES-LJJu
23
ES-LgS
14
ES-Ln2
14
GL-Dsk
22
IT-Lsm
22
RU-Cok
14
US-EML
173
US-Fcr
174
US-Hn3
175
US-ICh
176
US-ICt
US-Jol
778
US-Jo2
179
US-Rws
180
US-SRC
181
US-Ses
182
US-Sta
14
Us-
183
US-Wi6
184
US-Wi7
185
US-xHE
186
US-xJR
187
US-xMB
188
US-xNQ
189
US-xSR
190
Savannas (SAV)
AU-ASM
14
AU-Cpr ES-LMI
14
AU-DaS 14
AU-Dry 14
AU-GWW 14
CG-Tch 14
ES-Abr
23
23
ES-LM2 23
SD-Dem
SN-Dhr [44]
US-LS2 191
US-Wis
192
US-xSJ
193
Snow and Ice (SNO)
US-NGB 194
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Published as a conference paper at ICLR 2025
Table 5: EC Sites (cont'd)
Water Bodies (WAT)
US-Pnp 195
US-UM3 196
Permanent Wetlands (WET)
AR-TFI
197
AU-Fog
14
CA-ARB
CA-ARF
CA-CFI
200
CA-DB2
201
CA-DBB
202
CN-Ha2
14
CZ-wet
22
DE-Akm
25
DE-SIN
14
DE-Spw
4
DE-Zrk
14
DK-Skj
22
Fl-Lom
14
FI-Si
22
FR-LGt
22
GL-NuF
GL-ZaF
14
IE-Cra
23
PE-QFR
203
RU-Che
14
SE-Sto
22
SJ-Adv
14
UK-AMo
US-ALQ
204
US-Atq
14
US-BZB
203
US-BZF
206
US-B70
207
US-EDN
208
US-HBB
209
US-ICs
2TO
US-Ivo
14
US-KS3
211
US-Los
14
US-Myb
212
US-NC4
213
US-ORv
214
US-OWC
215
US-Srr
216
US-SU
US-Tw1
218
US-Tw4
219
US-Tw5
220
US-WPT
221
US-xBA
222
Woody Savannas (WSA)
AU-Ade [14
AU-Gin [4
AU-How 14
AU-RDF [4
BR-Npw 223
ES-Cnd 233
Table 6: Metcorological Variables in CarbonSense
Code
Description
Units
Predictors
TA_F
Air temperature
deg C
PA F
Atmospheiic pressure
kPa
P.F
Precipitation
mm
RH
Relative humidity
%
VPD_F
Vapor pressure deficit
hPa
WS_F
Wind speed
m s-1
USTAR
Wind shear
m s
WD
Wind direction
decimal degrees
NETRAD
Net radiation
W m -2
SW SW_IN_F
Incoming shortwave radiation
W m 2
SW WWOUT
Outgoing shortwave radiation
W m-
SW DIF
Incoming diffuse shortwave radiation
W m 2
LWIIN_F F
Incoming longwave radiation
m -2
LW_OUT
Outgoing longwave radiation
W m m-
PPFD IN
Incoming photosynthetic photon flux density Outgoing photosynthetic photon flux density Incoming diffuse photosynthetic photon flux density CO2 atmospheric concentration
umol Photon m x
PPFD OUT
umol Photon m 2 s
PPFD DIF
umol Photon 2
CO2 F FDMDS
umol CO2 mol
FDS G MDS
Soil heat flux Latent heat flux Sensible heat flux
W m
LE F MDS
W m
H_F_MDS
W m
Targets
NEE, VUT REF
Net Ecosystem Exchange (variable USTAR)
umol CO2 2 -11
GPP DT VUT REF
Gross Primary Production (daytime partitioning)
umol CO2 m -2 -2s- -1
GPP GPP_NT_ VUT NP__NT_VT_L REF
RUF_REF
Gross Primary Production (nighttime partitioning)
umol CO2 m
m
RECO_DS _T_ VUT REF
Ecosystem Respiration (daytime partitioning)
umol CO2 m 2 2 2-
RECO NTT VUT REF
Ecosystem Respiration (nighttime partitioning)
umol CO2 m -2 s-
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Temporal Distribution of CarbonSense EC Station Data
150
Number of site-vears
125
75
50
1990
1995
2000
2005
2010
2015
2020
Year
Figure 8: Temporal distribution of CarbonSense data. Data collected before 2000 does not have any MODIS data.
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Published as a conference paper at ICLR 2025
B EXPERIMENT DETAILS
B.1 DATASET CONFIGURATION
As part of the CarbonSense pipeline, we filter out poorly gapfilled values. Each variable in the EC data has corresponding "quality check" (QC) flag indicating if it was directly measured, gap filled during the ONEFlux pipeline (with varying gap fill quality levels), or simply taken from ERA5 reanalysis products. The tolerance level can be configured during the CarbonSense normalization process, and we discuss this further in the supplementary material.
We chose to use maximum QC flag of 1, indicating all values in the dataset were either directly measured, or gap filled with high confidence. We found this had the best trade-off of data quality and quantity, as setting the maximum QC flag to 0 (only directly measured values) reduced the dataset size by 55%, while including medium-confident values only increased by 9%.
The data split was randomized within each ecosystem type. We held out 20% sites for each type, whichever lower. The remaining sites were divided 80/20 between training and validation sets for EcoPerceiver, while our XGBoost model used all the training data for cross- validatio procedure.
B.2 ECOPERCEIVER CONFIGURATION
Hyperparameter tuning for EcoPerceiver was performed with our train and validation splits, and comprised the bulk of the experiment efforts. Where possible, we started with our best guesses and ran a pseudo-random search based on intuition. A true random search of the parameter space would have been extremely sparse given the available compute resources.
We set our latent hidden size to 128, our input embedding size to 16, and the number of Fourier encoding frequencies to 12. This gave a total input hidden size of 40. Our context window 32, meaning model sees the previous 32 hours of observations. We use 8 WCA blocks followed by 4 CSA blocks. We set our observational dropout at 0.3 and use causal masking in all self-attention blocks. In keeping with [26), employ weight sharing between all WCA blocks. With these hyperparameters our model weighs in at a very reasonable 988,633 parameters.
Heavier configurations were considered, but performance gains were minimal (see ablation studies) and the compute tradeoff made impractical for anyone without multi-GPU cluster access to use the model. This is especially true for increasing the context window latent hidden dimension.
B.3 XGBoOST CONFIGURATION
Hyperparameters were found by random search. We used the same train/test split as the EcoPerceiver experiment; the train set was used in 5-fold cross validation frame- work with 50 iterations. Once hyperparameters were found, retrained XGBoost on all training data before running inference on the test set. Table 7 details the pa- rameterization of our final model.
Table 7: XGBoost Hyperparameters
Parameter
Value
learning rate
0.1
alpha
0.1
gamma lambda
0.4
Since XGBoost is a tabular algorithm. we prepared geospa- tial data in similar fashion XBASE (4); each spectral band represents a single input value to the model. The value is obtained by taking weighted average of pixels based on Euclidean distance from the center of the image. Missing pixels were removed from this process, and the weights of the remaining pixels were increased to accom- modate for this. The code for this procedure provided with CarbonSense.
0.0
max _ex_
min child n. estimators
_hild_weighht weight
9
150
subsample
0.7
moole_pos_wette
0.5
colsample_be bytree
0.7
colsample haaumpl_bylevell
0.8
B.4 REPRODUCIBILITY AND RELIABILITY
Both EcoPerceiver and XGBoost were trained with re- producibility in mind. Once optimal hyperparameters were found, we performed 10 experiments
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Published as a conference paper at ICLR 2025
with each model in order to obtain reliable measure of performance (inspired by [224 ) Set seeds were provided to all frameworks utilizing RNG, and distributed dataloader workers were also seed-controlled to ensure full reproducibility of results.
The seeds for our experiments were simple integer values (0, 10, ...... 90) and were provided for the final training runs after hyperparameters had already been chosen.
B.5 DETAILED RESULTS
XiRass ExoPercaiver
NSE Scores by IGBP Type (Higher Better)
CSH
0.80
0.60
0.5
0.56
0.76
0.54
0.5
0.4
0.52
EBF
EVF
GRA
MF
DSH
0.82
0.83
0.78
0.82
0.77
0.50
0.81
0.78
0.6
0.35
0.73
0.75
-10
0.
0.45
0.65
0.40
0.0
-20
0.51
0.60
Figure 9: NSE scores of EcoPerceiver and XGBoost across different IGBP types. Each chart represents 10 experiments with different seeds. Whiskers indicate approximately 1.5 x interquartile range.
XGBaaZ ExoPercniver
RMSE Scores by IGBP Type (Lower is Better)
DBP
DNF
4.1
1.5
5.4
4.0
4.00
3.0
3.5
375
1/1
3.8
EBF
ENF
GRA
MF
OSH
3.9
2.6
32.
1.9
2.9
3.8
4.4
1.8
3.7
2.55
1.5
40
2.4
13
2.5
1.4
35
12
7.0
Figure 10: RMSE scores of EcoPerceiver and XGBoost across different IGBP types. Each chart represents 10 experiments with different seeds. Whiskers indicate approximately 1.5 x interquartile range.
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Here take closer look at the performance of our models across different IGBP types. Figure Iaa and Figure [1b show box plots our models' test set performance using NSE and RMSE respectively, across all seeds. Note that the y-axis changes between each plot. We do this because the variance in model performance across different seeds was generally small, and charting all box plots on the same axis made the chart unreadable.
As discussed Section 5 EcoPerceiver performs better than the XGBoost model in 12 out of 15 IGBP types. In of the 3 that XGBoost wins, both models do substantially worse than simply guessing the mean, which makes the results for WAT challenging to interpret. The other 2, ENF and WET, are not significantly better than EcoPerceiver's performance and have mean NSE advantages of +0071 and +0.0393 respectively in favour of XGBoost. This could be explained by the nature of data splitting; once hyperparameters were obtained for XGBoost, was able to train on the entirety of the train split, while EcoPerceiver still had to reserve 20% of the split for validation testing to measure convergence. Both ENF and WET had significant train set prevalence, so these represent IGBP types where XGBoost most able to take advantage of additional data.
Imperfect hyperparameter selection could also account for the lack consistent performance. While XGBoost is lightweight enough for virtually exhaustive parameter search with cross validation, deep models have significantly higher experiment overhead. Due to compute limitations, we were limited in how thoroughly could explore model configurations for EcoPerceiver.
B.6 ADDITIONAL MODEL COMPARISONS
Our primary experiments with EcoPerceiverrompar its performance with XGBoost baseline, as this is the model we found performed the best out of the tabular architectures. Here we show the comparative performance of these models against additional baselines including vanilla transformer, random forest, and simple linear regression model. The NSE and RMSE scores are shown in Tables and 9 respectively. We kept these results partitioned by IGBP type to demonstrate the consistency of the results. Note that since linear regression models are deterministic, this model was not trained with multiple seeds to find an average performance.
These results further reinforce the idea that XGBoost remains competitive baseline for DDCFM with non-deep learning methods. EcoPerceiver still demonstrates superior performance in the vast majority of IGBP types, but the vanilla transformer model notably wins out in WAT. This suggests that even without all the innovations of EcoPerceiver, deep learning models are highly effective at multimodal modelling in the context of DDCFM.
Table 8: NSE scores for all models.
IGBP
Linear Model
XGBoost Random Forest
Transformer
EcoPerceiver
CRO
0.6315
0.7292
0,8066
0,8126
0.8482
CSH
0.5072
0.7107
0.7510
0.7381
0.7670
CVM
0.4282
0,5179
0.5277
0,4809
0.5763
DBF
0.5333
0.6875
0.7250
0.7318
0.7547
DNF
0.2178
0.2975
0,2803
0,2745
0.4336
EBF
0.6381
0.7938
0.7966
0.7464
0.8220
ENF
0.5934
0.7375
0.7765
0.7154
0.7694
GRA
0.6264
0.7258
0.7461
0.6803
0.7967
MF
0.6043
0.7250
0.7559
0.7316
0.7717
OSH
0.0585
0.4113
0,5451
0.5050
0.6060
SAV
0.1632
0.4174
0.5802
0.5288
0.7368
SNO
-0.6223
-0.0130
-0,0370
-0.1229
0.2898
WAT
33.6151
-27.8940
-11.0524
-9.6445
-14,4010
WET
0.0976
0,2508
0.4530
0,4138
0.4137
WSA
0.4946
0.5575
0.6132
0.5560
0.6267
28

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Published as a conference paper at ICLR 2025
Table 9: RMSE scores for all models.
IGBP
Linear Model
XGBoost Random Forest
Transformer
EcoPerceiver
CRO
4.4698
3.8319
3.2381
3.1873
2.8677
CSH
2.1417
1.6411
1.5224
1.5613
1,4709
CVM
6.0688
5.5726
5.5157
5,7824
5.2236
DBF
5.3360
4.3661
4.0959
4.0451
3,8678
DNF
4.2721
4.0485
4.0974
4.1143
3.6322
EBF
6.1426
4.6365
4.6050
5.1420
4.3070
ENF
3.7959
3.0497
2.8141
3.1755
2.8579
GRA
3.9406
3,3759
3.2487
3.6451
2.9059
MF
4.9190
4.1002
3.8633
4.0511
3.7361
OSH
2.7043
2.1384
1.8796
1.9609
1.7475
SAV
2.3315
1.9455
1.6514
1.7497
1.3070
SNO
1.7876
1,4126
1,4291
1,4873
1,1816
WAT
5.3247
4.9366
3.1838
3.0019
3.5802
WET
2.8352
2.5834
2,2073
2.2851
2.2830
WSA
2.8752
2.6903
2.5153
2.6952
2.4706
B.7 ABLATION STUDIES
Here we present ablation studies on EcoPerceiver. While we seek to provide broad view of the impacts of our architectural decisions, note that running all these tests with large number of seeds was computationally infeasible for us. Instead we ran each ablation test with single seed ("00") and plotted performance below. The boxplots in this section therefore represent scores across different IGBP types.
To improve interpretability, the single WAT site was removed from the set for these ablation studies. As discussed in Section this is one-shot EC station with ecosystem dynamics far out of distribution compared to other biomes. Model performance on this one site was exceedingly poor and variable, with NSE scores ranging from -3.0 to -20.2. Therefore, when running single experiment per model, it would have a massively outsized and unpredictable effect on model scores.
B.7.1 ARCHITECTURAL CHARACTERISTICS
Figure I1 shows a broad comparison various architectural configurations on performance. We break down statistics for each architecture in Table I0 Each of these models was meant interrogate the decisions we made when constructing the final EcoPerceiver. A summary of each model follows:
Ours: This the final version of EcoPerceiver, run with a single seed.
No Causal Masking: A model where latent tokens are allowed to self-attend non-causally.
No Observational Dropout: A model where all observations are kept during training, rather than randomly masking a portion of them during windowed cross-attention.
No Fourier Encoding: model with Fourier encoding. This means the final input vector for cross-attention is of length H; = lemb + 1.
Gapfilled Data: This ablation used our final model architecture, but used a version of CarbonSense where we keep the gapfilled values from the underlying ONEFlux pipeline
rather than handling missing data within the model.
No Image Data: This ablation also uses our final model, but does not pass in any image data during training inference. operates entirely on tabular data.
Vanilla Transformer: A model without WCA or CSA. is a transformer encoder as described in (25). Each forward pass ingests the tabular and image data (tokenized as in our final model) for single timestep. Regression performed via linear probing on the latent space after 6 encoder layers. This is the best approximation of vanilla transformer encoder in the context of our multimodal dataset, and is the model used in the model comparison in Appendix B.6
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Published as a conference paper at ICLR 2025
Run
Ours
No Causal Masking
1.0
No Observational Dropout
No Fourler Encoding
NSE (Higher is Better)
Gaphilled Data
No Image Data
Vanillà Transfommer
0.6
0.4
0.2
(a)
Causal Masking
Observational Dropout
Encoding
RMSE (Lower is Better)
Gapfilled
Image
Vanilla Transformer
3
(b)
Figure 11: Boxplots of NSE (a) and RMSE (b) scores for ablation studies.
Our model performs better than all other ablation models broadly. The largest jump in performance is between the vanilla transformer and all other architectures. This indicates that WCA has substantial effect on the ability of the model to predict carbon fluxes. It is unclear whether this benefit comes from the way WCA processes information and maps the latent space, or whether this is reflection of the non- non- Man-Marrovvin nature of ecosystem dynamics. Since the vanilla transformer only consumes data from the timestep for which it predicting fluxes, not capturing the context window of observations, and designing vanilla transformer to ingest full context window would result in an intractable amount of processing for the forward pass.
The model without image data also underperformed, suggesting that satellite imagery provides substantial information with respect to carbon flux prediction. Additionally, the gapfilled data experiment shows that EcoPerceiver is able to process missing data in-model effectively. The remaining ablations (fourier encoding, observational dropout, causal masking) demonstrate that these architectural considerations may provide modest benefit, though the results are far closer.
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Published as a conference paper at ICLR 2025
Table 10: NSE and RMSE by model ablation. t_maan and r std represent truncated means and
standard deviations account for the outlier biome ("WAT").
NSE
RMSE
Model
meam
s_t
median
iqr
mean
L_d
median
iqr
Vanilla Transformer
0.557
0.249
0.556
0.284
2.709
1.165
2.648
1.603
No Image Data
0.635
0.186
0.689
0.215
2.515
1.134
2.478
1.582
Gapfilled Data
0.635
0.199
0.717
0.259
2.474
1.077
2.376
1.492
No Fourier Encoding
0.624
0.254
0.715
0.257
2.456
1.043
2.481
1.098
No Obs. Dropout
0.634
0.196
0.710
0.345
2.468
1.091
2.339
1.105
No Causal Masking
0.654
0.171
0.744
0.318
2.428
1.077
2.252
0.836
Ours
0.669
0.166
0.753
0.251
2.382
1.055
2.313
1.284
In particular, the causal masking performed roughly equivalently our final model. However, decided to keep the causal masking in the final model anyway as it did not affect wall time, and contributed to the model's ecological validity.
B.7.2 CONTEXT WINDOW LENGTII
We conducted further experiments test the assumption that DDCFM benefits from ingesting data
in temporal context window. The results are shown in Figure [2] There is a clear performance advantage going from context window of hours 16, and a small advantage going from 16 to 32. Anecdotally, this reflects our findings from early hyperparameter tuning experiments. Going from 32 64 did not meaningfully improve performance, but it did significantly increase our wall time and memory usage. We therefore used a context window of 32 for our main experiments.
16-
NSF (Higher ic Better)
RMSE (Lower is Better)
64
02
Figure 12: Effects context window length on NSE (left) and RMSE (right) of test site inference for EcoPerceiver.
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Published as a conference paper at ICLR 2025
B.8 PESSYSTEM-SPECIERE DDCFM
DDCFM is not always performed at the global scale; many research teams have studied it in the context of specific regions and ecosystem types [5] 6 9 [2]. This use case one of the reasons for CarbonSense partitioned struc- ture. As a proof of concept, we ran a single experiment with EcoPerceiver where we use the same parameters and train/test split as our main experiment, but only include the DBF sites. We then compared the test set performance against our main model. Table [1 shows the results our model trained on multiple ecosystem types had notably better performance despite similar convergence time.
Table 11: EcoPerceiver performance on DBF sites when trained on only DBF data vs trained on all sites
Only DBF
All Sites
NSE
RMSE
NSE
RMSE
0.7405
3,9782
0.7532
3.8806
As with the ablation studies, we did not have the compute resources to do 10 seeds for every experiment variation but it shows the flexibility of CarbonSense for different research scenarios. It also provides preliminary evidence that DDCFM with multimodal models may benefit from adding more training data even if it is relatively out of distribution.
B.9 QUALITATIVE ANALYSIS
While етrоr metrics are useful for assessing the aggregate performance of the model, we encourage researchers inspect the model outputs in comparison the observed data. As an example, consider Figures 13 and 14 below. Both of these were randomly selected 4-day stretches of data from their respective sites. Both models appear able to model GF-Guy very well, but not CA-LPI, and this may be counterintuitive first glance. But there's 's quite bit going here.
GF-Guy is an evergreen broadleaf forest in the tropics. This is not highly prevalent type in Carbon- Sense, but its carbon fluxes appear quite stable from day to day. We found that ecosystems with this interseasonal stability tend to be more easily modelled in our experiments, though this is not an easy metric to quantify. It should be noted that the y-axis has much larger scale, while the models appear close to the ground truth, they often have an error in excess of 5 umol CO2 m 22, This again highlights the importance of using NSE as error metric RMSE will unfairly punish highly active ecosystems like this due to higher natural variance in carbon fluxes.
CA-LP1 is evergreen needleleaf forest in the temperate region, which is one of the best represented ecosystems in CarbonSense, yet both models struggle with it (despite low absolute error), especially in the winter. Reading into this site reveals is a pine beetle-attacked forest (97]: disturbances like these can be challenging to model as we discussed in 2 This gives future work in DDCFM a vector for potential improvement.
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Published as a conference paper at ICLR 2025
GF- Guy. 2022-01-31- 2022-01-05
ME
-10
Model
XGDoost EcoParcoivar Ground
-30
2022-01-0 07/00009
2022-01-11 19:00.00
2022-01-2 07 01:0000
2022-01-22 13,00.00
2022-01-33 07:06.00
2022-01-8 19:00.00
2022-01 04 07:00:00
2022-01-44 19: 19:00:00
Time
Figure 13: Hourly data and model results for GF-Guy, evergreen broadleaf forest station in French Guiana.
CA-1P1: 20-5-12:06- 2015-12-10
1.0
0.5
MEE
-0.5
Model
XG3oost
EcoPorcalvor
-1.0
Truth
2015-12-06 13:0.00
2015-12-07 01:0000
2015:12-97 13:000
2015-12-45 00:00
2015-12-06 23.00:0
2015-12-09 01:03a9
2015-12-09 13:0000
001--22-100010098
Time
Figure 14: Hourly data and model results for CA-LPI, a pine beetle-attacked evergreen needleleaf forest in northern British Columbia.
33


Reviews:
[{"review_id": "qTr0Sdlwz9", "reviewer": "Reviewer_yJf4", "summary": "This paper proposes a standardized dataset called CarbonSense, which is a dataset compiled from various sources. The compilation steps include fusing the data together on the same hourly scale, extracting relevant features, and doing min max normalization. Along with the dataset, the paper also proposes a data driven model based on Perceiver called EcoPerceiver. This proposed model uses transformer architecture to cross attend all input features. The paper also implemented a SOTA approach baseline and compared its performance on the proposed dataset with EcoPerceiver.", "questions": "How does the Perceiver model compare to other advanced machine learning models in terms of performance? In addition, which component of EcoPerceiver affects performance the most? I think adding more machine learning model baselines and conducting ablation study would help determine the model's relative strengths and weaknesses.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "CarbonSense integrates diverse data modalities—measured carbon fluxes, meteorological predictors, and satellite imagery—across a wide array of ecosystems. Researchers can use this dataset as a standardized benchmark.", "weaknesses": "1.The dataset was compiled from multiple sources with various modalities, which may introduce inconsistency or OOD samples when doing model training. Careful data analysis can be helpful\n2. The experiment shows the proposed EcoPerceiver outperformed the current SOTA approach for most IGBP types especially WET, WAT, and ENF. However, the paper did not include an ablation study to showcase why the proposed model achieved this performance.\n3.There is only one baseline compared and there is no model with single modalities.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "q2WbvbXmoB", "reviewer": "Reviewer_ZVzD", "summary": "Authors present CarbonSense as a machine learding ready multimodal dataset for carbon flux modeling. The dataset includes meteorological variables, MODIS 81 pixels satellite observations, and eddy covariance of carbon fluxes from hundreds of locations globally, Authors, in addition to the proposed dataset, presented a baseline machine learning model to predict carbon fluxes. The model shows the power of the multimodality of the dataset to improve canbon flux modeling performance.", "questions": "* Consider adding satellite date from Sentinel 2 and 2 or Landsat which are higher resilution for days available. These datasets are open for anyone to use.\n* Is it possible to keep expanding the dataset in a systematic way by ingesting EC and MODIS data periodically?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "* Paper is well written and cohesive. It is easy to follow and understand even for non-experts in the field\n* Adds a clear contribution to existing dataset in terms of scale and modalities added. This will definitely help progress in the field.\n* Dataset will be open for anyone to use. This is important for it to make any impact.", "weaknesses": "* The satellite imagery added is very low resolution wich limits it's potential usefulness. \n* The dataset does not include many observations outside developed countries. Nothing the authors can do sinse they leverage existing EC stations available.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "IOUvuQQoZw", "reviewer": "Reviewer_8jPr", "summary": "This paper presents a new benchmark dataset, Carbon Sense, for predicting carbon flux from geospatial and meteorological data. Two models are evaluated on the new benchmark: 1) an XGBoost model which emulates the SOTA method from prior work, and 2) the EcoPerceiver model proposed in this paper. Domain-relevant evaluation metrics of RMSE and NSE (Nash-Sutcliffe Modelling Efficiency) are reported for each model and broken down by ecosystem type, with statistical tests of significance of the differences in performance. EcoPerceiver significantly outperforms the XGBoost model, indicating there is room for improvement on the benchmark relative to the previous SOTA with more sophisticated/tailored ML approaches.", "questions": "- The train/test splits are divided by station location, which avoids spatial autocorrelation issues. It seems there could be significant temporal autocorrelation within each split since there are many measurements from the same location. Is temporal autocorrelation a concern?\n\n- What does the purple vector in Figure 4 represent?\n\n- How do other deep learning models (existing models besides the new EcoPerceiver) perform on CarbonSense?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The benchmark has multimodal inputs, which helps fill a notable gap in multimodal benchmarks for remote sensing/geospatial ML. \n- The paper presents useful and approachable background about the carbon flux modeling problem.\n- The benchmark code is designed to allow flexibility in reproducing and extending or modifying the dataset based on user needs.\n- The benchmark has a permissive CC-BY license.\n- The EcoPerceiver method is well motivated based on the domain-specific carbon flux modeling problem.\n- Domain relevant metrics are used for evaluation.", "weaknesses": "- The train/test splits are divided by station location, which avoids spatial autocorrelation issues. It seems there could be significant temporal autocorrelation within each split since there are many measurements from the same location. Is temporal autocorrelation a concern?\n- The experiments only compared two models, XGBoost and EcoPerceiver. It would be useful to see additional models benchmarked (especially deep learning models) to get a sense of the variation in performance existing solutions on this benchmark.\n- What does the purple vector in Figure 4 represent?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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:

Decision: Accept (Poster)