# Using Geostationary Satellite Observations and Machine Learning Models to Estimate Ecosystem Carbon Uptake and Respiration at Half Hourly Time Steps at Eddy Covariance Sites

Sadegh Ranjbar, Daniele Losos, Sophie Hoffman, Matthias Cuntz, Paul C Stoy

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Sadegh Ranjbar, Daniele Losos, Sophie Hoffman, Matthias Cuntz, Paul C Stoy. Using Geostationary Satellite Observations and Machine Learning Models to Estimate Ecosystem Carbon Uptake and Respiration at Half Hourly Time Steps at Eddy Covariance Sites. Journal of Advances in Modeling Earth Systems, 2024, 16 (10), pp.e2024MS004341. 10.1029/2024MS004341. hal-05077598

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RESEARCH ARTICLE

10.1029/2024MS004341

**Key Points:**

* Advanced Baseline Imager (ABI) observations can estimate sub-daily ecosystem carbon uptake and respiration at 126 AmeriFlux sites

* Integration of top-of-atmosphere products enhances the accuracy of monitoring land surface functions

* ABI observations can fill eddy covariance data gaps: up to 1 week (high accuracy), 6 months (low accuracy)

**Supporting Information:**

Supporting Information may be found in the online version of this article.

**Correspondence to:**

S. Ranjbar,
sranjbar@wisc.edu

**Citation:**

Ranjbar, S., Losos, D., Hoffman, S., Cuntz, M., & Stoy, P. C. (2024). Using geostationary satellite observations and machine learning models to estimate ecosystem carbon uptake and respiration at half hourly time steps at eddy covariance sites. *Journal of Advances in Modeling Earth Systems, 16*, e2024MS004341.
https://doi.org/10.1029/2024MS004341

Received 14 MAR 2024
Accepted 7 OCT 2024

**Author Contributions:**

**Conceptualization:** Paul C. Stoy

**Formal analysis:** Paul C. Stoy

**Funding acquisition:** Paul C. Stoy

**Investigation:** Matthias Cuntz

**Methodology:** Paul C. Stoy

**Resources:** Paul C. Stoy

**Supervision:** Paul C. Stoy

**Writing – original draft:** Paul C. Stoy

**Writing – review & editing:**
Matthias Cuntz, Paul C. Stoy

© 2024 The Author(s). Journal of Advances in Modeling Earth Systems published by Wiley Periodicals LLC on behalf of American Geophysical Union. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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# Using Geostationary Satellite Observations and Machine Learning Models to Estimate Ecosystem Carbon Uptake and Respiration at Half Hourly Time Steps at Eddy Covariance Sites

Sadegh Ranjbar¹ ID, Daniele Losos¹, Sophie Hoffman¹ ID, Matthias Cuntz² ID, and Paul C. Stoy¹ ID

¹Department of Biological Systems Engineering, University of Wisconsin—Madison, Madison, WI, USA, ²Université de Lorraine, AgroParisTech, INRAE, UMR Silva, Nancy, France

**Abstract** Polar-orbiting satellites have significantly improved our understanding of the terrestrial carbon cycle, yet they are not designed to observe sub-daily dynamics that can provide unique insight into carbon cycle processes. Geostationary satellites offer remote sensing capabilities at temporal resolutions of 5-min, or even less. This study explores the use of geostationary satellite data acquired by the Geostationary Operational Environmental Satellite—R Series (GOES-R) to estimate terrestrial gross primary productivity (GPP) and ecosystem respiration (RECO) using machine learning. We collected and processed data from 126 AmeriFlux eddy covariance towers in the Contiguous United States synchronized with imagery from the GOES-R Advanced Baseline Imager (ABI) from 2017 to 2022 to develop ML models and assess their performance. Tree-based ensemble regressions showed promising performance for predicting GPP (R² of 0.70 ± 0.11 and RMSE of 4.04 ± 1.65 µmol m⁻² s⁻¹) and RECO (R² of 0.77 ± 0.10 and RMSE of 0.90 ± 0.49 µmol m⁻² s⁻¹) on a half-hourly time step using GOES-R surface products and top-of-atmosphere observations. Our findings align with global efforts to utilize geostationary satellites to improve carbon flux estimation and provide insight into how to estimate terrestrial carbon dioxide fluxes in near-real time.

**Plain Language Summary** Fighting climate change requires an understanding of how ecosystems absorb and release carbon dioxide. While most Earth-orbiting satellites provide limited snapshots, this study explores how more frequent imagery—every 5 min—from geostationary satellites, also known as weather satellites, can be used to estimate ecosystem carbon dioxide flux. By combining this data with machine learning techniques, we successfully estimated carbon uptake and release at over 100 US sites at half-hourly intervals. This paves the way for near-real-time global monitoring of carbon exchange, offering a powerful tool for scientists and policymakers tackling climate change.

## 1. Introduction

Satellite remote sensing has revolutionized our understanding of the terrestrial carbon cycle (Xiao et al., 2019) and demonstrated the critical role of terrestrial carbon fluxes in determining the trends and variability of atmospheric carbon dioxide concentration (Friedlingstein et al., 2022; Z. Zhu et al., 2016). Many of these insights come from polar-orbiting satellites like MODIS (see Table 1), whose observations have been used to estimate key carbon cycle processes like gross primary productivity (GPP) and ecosystem respiration (RECO), typically on time scales of weeks to years (Running & Zhao, 2015; Running et al., 2004; Turner et al., 2006). These algorithms are being continually improved using GPP and RECO estimates from eddy covariance measurements (Heinsch et al., 2006; Huang et al., 2021; L. Wang et al., 2017), all of which have led to a greater appreciation of the role ecosystems play in the global carbon cycle and the critical climate services they provide (Novick et al., 2022).

Despite the key contributions of polar-orbiting satellites in carbon cycle research, a number of challenges remain, especially with respect to changes to carbon cycle processes that happen quickly. Rapid phenological transitions remain difficult to observe and model (Richardson et al., 2013). Extreme events play a disproportionate role in carbon cycling at ecosystem to global scales (Reichstein et al., 2013; Zscheischler et al., 2014), but their full impacts can be difficult to ascertain with intermittent measurements, especially when cloudiness reduces data availability (Jeong et al., 2023). Observing diurnal patterns in variables linked to plasnt stres help fully understand the impacts of drought and other disturbances (X. Li et al., 2023; Xiao et al., 2021). Model-data integration efforts
<table><caption>**Table 1**
A List of Abbreviations Used in This Study</caption><thead><tr><td>Abbreviation</td><td>Full form</td></tr></thead><tbody><tr><td>ABI</td><td>Advanced Baseline Imager</td></tr><tr><td>BRF</td><td>Bidirectional Reflectance Factor</td></tr><tr><td>CMI</td><td>Cloud and Moisture Imagery</td></tr><tr><td>DSR</td><td>Downwelling Solar Radiation</td></tr><tr><td>EC</td><td>Eddy Covariance</td></tr><tr><td>EDI</td><td>Environmental Data Initiative</td></tr><tr><td>GBR</td><td>Gradient Boosting Regression</td></tr><tr><td>GOES-R</td><td>Geostationary Operational Environmental Satellite-R Series</td></tr><tr><td>GPP</td><td>Gross Primary Productivity</td></tr><tr><td>IGBP</td><td>International Geosphere Biosphere Programme</td></tr><tr><td>LST</td><td>Land Surface Temperature</td></tr><tr><td>LOOCV</td><td>Leave-One-Out Cross-Validation</td></tr><tr><td>MODIS</td><td>Moderate Resolution Imaging Spectroradiometer</td></tr><tr><td>MAE</td><td>Mean Absolute Error</td></tr><tr><td>MLP</td><td>Multi-Layer Perceptron</td></tr><tr><td>ML</td><td>Machine Learning</td></tr><tr><td>NN</td><td>Neural Network</td></tr><tr><td>NDVI</td><td>Normalized Difference Vegetation Index</td></tr><tr><td>NIRv</td><td>Near Infrared Reflectance of Vegetation</td></tr><tr><td>NIRvP</td><td>NIRv multiplied by PAR</td></tr><tr><td>PAR</td><td>Photosynthetically Active Radiation</td></tr><tr><td>R<sup>2</sup></td><td>Coefficient of Determination (R-squared)</td></tr><tr><td>RECO</td><td>Ecosystem Respiration</td></tr><tr><td>RF</td><td>Random Forest</td></tr><tr><td>RMSE</td><td>Root Mean Square Error</td></tr><tr><td>SVR</td><td>Support Vector Regression</td></tr><tr><td>SNR</td><td>Signal-to-Noise Ratio</td></tr><tr><td>TOA</td><td>Top-of-Atmosphere</td></tr></tbody></table>

are increasingly recognizing the importance of sub-daily processes. Recent developments in data-driven terrestrial land-atmosphere flux modeling through the FLUXCOM-X framework and X-BASE products (Nelson et al., 2024) offer high-resolution estimates of terrestrial carbon dioxide and water vapor fluxes, with up to 0.05° spatial and hourly temporal resolution.

To enable near real-time carbon cycle prediction, geostationary (“weather”) satellites are increasingly being used to estimate land surface and carbon cycle processes on time scales as short as minutes (Desai et al., 2021; Jeong et al., 2023; Khan et al., 2022; X. Li et al., 2023; R. Nemani et al., 2020; Ranjbar et al., 2024; M. Wang et al., 2020; W. Wang et al., 2020; Xiao et al., 2021). “Hypertemporal” remote sensing (Miura et al., 2019) may better capture the impacts of rapid changes on land surface properties including those related to carbon cycling (Khan et al., 2021), and serve as a powerful new tool for understanding our changing carbon cycle (Xiao et al., 2021).

Measuring variables related to carbon cycle processes from geostationary satellites has become increasingly feasible, thanks to the enhanced spectral capabilities of new imagers in capturing data in near-infrared spectrum (Schmit & Gunshor, 2020; Schmit et al., 2017). The latest generation of sensors mounted on geostationary satellites, like the Advanced Baseline Imager (ABI) mounted on the Geostationary Operational Environmental Satellite—R Series (GOES-R) and Advanced Himawari Imager (AHI) mounted on Himawari 8/9, can capture data in the visible, near-infrared and infrared (Schmit & Gunshor, 2020). These observations can be used to calculate the normalized difference vegetation index (NDVI, Wheeler & Dietze, 2021), which is related to the leaf area index and therefore to GPP. Geostationary satellite data products can also be used to derive the near infrared reflectance of vegetation (NIRv), which is more closely related to GPP than NDVI alone, even in pixels with mixed vegetation types (Badgley et al., 2017, 2019). Such vegetation indices can be coupled with key products like land surface temperature (LST) and downwelling shortwave radiation (DSR) that have been developed from geostationary satellite observations for decades (Diak & Gautier, 1983; Gautier et al., 1980; D. Wang & Li, 2022). Coupling these variables can help develop models for GPP and RECO that are capable of capturing diurnal, seasonal, and interannual patterns in the terrestrial carbon cycle (Jeong et al., 2023; Khan et al., 2022).

There is no current synthesis on how to best employ geostationary satellite observations to estimate GPP and RECO across multiple ecosystems, but notable recent progress has been made in using geostationary satellites to estimate GPP at single sites or a suite of sites. Xiao et al. (2021) demonstrated how observations from the Advanced Himawari Imager can be used to infer diurnal patterns in regional GPP using a light use efficiency model similar to

the MODIS GPP algorithm across large swaths of Australia. Khan et al. (2022) noted that a model based on the NIRv multiplied by photosynthetically active radiation (NIRvP, Dechant et al., 2022) outperformed light use efficiency models when estimating GPP from eddy covariance measurements at a savanna ecosystem in California using observations from the ABI on the GOES-R series satellites. Jeong et al. (2023) demonstrated how a GPP model also based on NIRvP derived from GeoKompsat-2A observations can capture the diurnal and seasonal patterns of GPP estimated from multiple ecosystems in Korea. These findings highlight the potential for geostationary satellites to estimate GPP on time scales of minutes using visible and near infrared surface reflectance products coupled with DSR as an estimate of photosynthetically active radiation. It remains unclear how well geostationary satellites can estimate RECO, or the degree to which Machine Learning (ML) techniques can improve predictive skill over established methods given their promise for integrating eddy covariance and remote sensing observations (Jung et al., 2020; J. Li et al., 2022; L. Li et al., 2022; T. Yu et al., 2021). It is also unclear if additional measurements from geostationary satellites—like measurements in the shortwave infrared
<figure><img src="image_3.png" /><figcaption>**Figure 1.** The workflow used in the present study.</figcaption></figure>

and infrared related to atmospheric characteristics that are infrequently used for carbon cycle science—may further improve terrestrial carbon flux estimates. It is crucial to develop a comprehensive understanding of how to effectively utilize geostationary satellite observations to enhance terrestrial ecosystem science (Khan et al., 2021).

Here, we start to answer these questions by asking: (a) How accurately can ML techniques developed using GOES-R ABI-measured reflectances, brightness temperatures, and data products model GPP and RECO? (b) How do these models perform when estimating GPP and RECO from hundreds of eddy covariance tower sites? (c) What is the impact of different input variables on the performance of these machine learning models? We test the ability of different ML approaches that use different inputs from both surface data products and top-of-atmosphere (TOA) reflectances to estimate GPP and RECO using a Leave-One-Out Cross-Validation (LOOCV) algorithm (Maxwell et al., 2018) and goodness-of-fit metrics. We focus our discussion on the approaches and data sets that are necessary to further improve geostationary satellite carbon cycle science with an eye toward developing real-time estimates of terrestrial carbon fluxes and their associated uncertainties.

# 2. Methodology

## 2.1. Overview

The study follows a structured approach consisting of four main steps, as illustrated in Figure 1. We initially collected data from two sources—Ameriflux eddy covariance towers and the GOES-R ABI—and then pre-processed the data as described in Sections 2.2 and 2.3. Subsequently, we aligned the processed data in time and stored it in a repository with the Environmental Data Initiative (EDI), resulting in a unified, accessible data set as described in Losos et al. (2024). We employed ML algorithms to establish relationships between synchronized GOES-R observations and flux data, including GPP and RECO, as detailed in Section 2.4. In the final step, we trained the ML models and evaluated their performance using statistical metrics, specifically R² and RMSE, as
<figure><img src="image_4.png" /><figcaption>**Figure 2.** The spatial distribution of AmeriFlux and NEON eddy covariance sites used in this study, mapped over IGBP vegetation types for sites in the GOES-16 Full Disk projection.</figcaption></figure>

described in Section 2.5. The performance of the best model was assessed across different vegetation cover and seasons, while also investigating the model's sensitivity to missing measurements of different lengths ("gaps") in the eddy covariance data records following Moffat et al. (2007).

## 2.2. Estimating GPP and RECO From Eddy Covariance Data

From a total of 314 AmeriFlux and NEON eddy covariance towers discussed in Losos et al. (2024), we selected sites with a sufficient amount of eddy covariance data to partition carbon dioxide flux measurements into GPP and RECO for the period 2017 to 2022, which resulted in 126 sites (see Table S1 and Figure 2). We partitioned fluxes using the *hesseflux* library (Cuntz, 2020) in Python, which was created to process eddy covariance data in a similar way to the R-package REddyProc (Wutzler et al., 2018). *Hesseflux* is designed to perform the standardized processing steps described in Papale et al. (2006), for calculating half-hourly (or hourly) GPP and RECO and their uncertainty estimates, using both daytime (Lasslop et al., 2010) and nighttime (Reichstein et al., 2005) partitioning approaches.

This partitioning is completed after filtering the data for varying levels of data quality, including insufficient turbulence at night using a friction velocity (u*) filter (Reichstein et al., 2012). We elected to exclude forest sites that were unable to provide the in-canopy CO₂ concentrations used to calculate the storage flux in addition to the eddy flux, as GPP and RECO may be underestimated when excluding carbon storage from forest site measurements (Fu et al., 2018). We describe the workflow used to partition GPP and RECO at the Ameriflux and NEON network scale in Table S1 and Text S1 in Supporting Information S1. In total, observations from 1,392,490 time points collected from the 126 sites dating back to 2017 were used in this study.
## 2.3. GOES-R and the ABI

The ABI measures radiation at 16 spectral bands from the visible to infrared (0.47–13.3 µm) spectra, with nominal spatial resolutions between 0.5 and 2 km at nadir. In the operational scan mode (Mode 6), the ABI images the Western Hemisphere (full disk) at 10 min intervals, the Contiguous United States (CONUS) at 5 min intervals, and 1,000 × 1,000 km mesoscale domains at 30 s intervals that can be repositioned to monitor key meteorological events (Schmit & Gunshor, 2020; Schmit et al., 2017). We used observations from ABI aboard GOES-16 in the present analysis because it became operational first on 18 December 2017, and because GOES-17 was replaced by GOES-18 in 2022 due to overheating caused by a malfunctioning loop heat pipe (McCorkel et al., 2019). Prior to April 2019, the full disk was scanned every 15-min (Mode 3) rather than every 10-min. For the entire period, we averaged the two or three full disk measurements over the half-hour, and aligned the half-hourly ABI imagery with the half-hourly Ameriflux data. Additionally, we spatially aligned ABI pixels with tower locations by terrain-correcting to account for the parallax effect. Both data alignment processes are described in Losos et al. (2024). For further information about Level 1b and 2+ ABI products and their projections see Supporting Information S1 (Text S2).

In this study, besides the visible and NIR electromagnetic spectrum's surface products, including ABI bidirectional reflectance factors (BRF) bands 1, 2, and 3 (BRF1, BRF2, and BRF3 at 0.47, 0.64, and 0.86 µm), we also examined short-wave infrared (SWIR) bands to determine whether they might provide additional information that could be valuable for estimating GPP and RECO. Within the SWIR electromagnetic spectrum, the ABI sensor acquires two BRF bands: BRF5, Short-Wave Infrared 1 (SWIR1) at 1.6 µm, and BRF6, Short-Wave Infrared 2 (SWIR2) at 2.2 µm. Additionally, we used BRF2, BRF3, and DSR (Laszlo et al., 2020) to calculate NIRvP following (Losos et al., 2024). We note that this approximation introduces uncertainty, primarily related to the conversion from the DSR product to PAR, which is influenced by various factors, including atmospheric conditions and spectral differences in solar irradiance (Dechant et al., 2022; Losos et al., 2024). However, the practical impact of these uncertainties is generally minimal (Hwang et al., 2022; Laliberté et al., 2016).

We further explored 16 TOA bands, including visible, NIR, and TIR, to analyze their impact on GPP and RECO estimation. Specifically, we focused on the significance of TOA infrared bands, influenced by water vapor, clouds, atmospheric, and land surface temperature, as discussed in previous studies (Ai et al., 2018, 2020; Y. Yu & Yu, 2020; Y. Yu et al., 2008, 2011). However, a challenge arose due to the differing time series lengths of BRF and CMI products. While BRF data became operational in August 2021, CMI records date back to July 2017. Despite BRF's robust measurements less influenced by atmospheric processes, its shorter archive lacks the historical record richness of CMI time series, impacting model training. Therefore, we explored both products individually and in combination within our ML approaches.

## 2.4. Machine Learning Models

We considered four commonly used ML regression models for handling nonlinear problems; Random Forest (RF), Gradient Boosting Regression (GBR), Support Vector Regression (SVR), and Multi-Layer Perceptron Neural Network (MLP). All of these models are widely employed for estimation problems in data-driven approaches (Diaz-Gonzalez et al., 2022; Thapa et al., 2023; Varghese et al., 2023).

RF and GBR are ensemble learning models that combine decision trees through bootstrapping and feature bagging (Friedman, 2001), and are frequently employed in remote sensing (Ranjbar et al., 2024; Sahin, 2020; Saini & Ghosh, 2017; Y. Zhang et al., 2022). They offer feature importance measures, enabling us to assess the significance of different features. However, they differ in their approach and emphasis. RF focuses on simplicity and minimizing training set loss, while GBR puts more emphasis on optimizing a loss function to minimize errors by creating an ensemble of weak models, sequentially improving upon the mistakes made by previous models (Bentéjac et al., 2021; Sahin, 2020). RF and GBR both require hyperparameter tuning for optimized modeling. The number of trees in the ensemble determines the size and complexity of the model, and the maximum depth of each tree determines the depth of the decision trees in the ensemble (Sahin, 2020; Saini & Ghosh, 2017; Y. Zhang et al., 2022).

SVR, a regression algorithm based on Support Vector Machines (SVM), identifies the best hyperplane in a high-dimensional feature space in order to maximize the margin between data points (Mountrakis et al., 2011; Smola & Schölkopf, 2004), minimizing prediction error (Mountrakis et al., 2011). The SVR algorithm can model both
<table><caption>**Table 2**
Grid Search Specifications and Searching Range for Hyperparameters of the Machine Learning Models</caption><thead><tr><th>Model</th><th>Hyperparameter</th><th>Search range</th></tr></thead><tbody><tr><td rowspan="2">RF</td><td>Number of estimators</td><td>100, 300, 500, 800, 1,000</td></tr><tr><td>Maximum depth</td><td>5, 10, 15, 20, 50, 100</td></tr><tr><td rowspan="3">GBR</td><td>Number of estimators</td><td>50, 100, 200, 400</td></tr><tr><td>Maximum depth</td><td>3, 5, 8, 10</td></tr><tr><td>Learning rate</td><td>0.01, 0.05, 0.1</td></tr><tr><td rowspan="5">MLP</td><td>Activation</td><td>"logistic," "tanh," "relu"</td></tr><tr><td>Solver</td><td>"adam," "sgd"</td></tr><tr><td>Hidden layer</td><td>2, 3, 5</td></tr><tr><td>Hidden layer size</td><td>50, 100, 200, 500</td></tr><tr><td>Alpha</td><td>0.0001, 0.001, 0.01, 0.1</td></tr><tr><td rowspan="4">SVR</td><td>Penalty</td><td>0.1, 10, 100, 1,000</td></tr><tr><td>Epsilon</td><td>0.001, 0.01, 0.2</td></tr><tr><td>Gamma</td><td>0.01, 0.1, 0.5, 1, 10</td></tr><tr><td>Kernel</td><td>"RBF," "sigmoid," "poly," "linear"</td></tr></tbody></table>

linear and non-linear relationships using various kernels, including Radial Basis Function (RBF), sigmoid, polynomial, and linear. Key hyperparameters for SVR include penalty ("C"), gamma, epsilon, and, in the case of polynomial kernels, the degree parameter (Ranjbar et al., 2021). The C parameter controls regularization strength, gamma influences the shape of the decision function, epsilon sets the tolerance margin, and the degree determines the complexity of the polynomial kernel (Mountrakis et al., 2011).

Lastly, MLP is an artificial neural network that learns complex patterns and relationships through backpropagation, making it a flexible tool for approximating non-linear relationships (Baraldi et al., 2001; Gardner & Dorling, 1998; Ranjbar et al., 2021; Suliman & Zhang, 2015). It consists of multiple layers of interconnected "neurons" and utilizes non-linear activation functions to model intricate relationships between inputs and outputs (Baraldi et al., 2001; Suliman & Zhang, 2015). MLP also has hyperparameters, including the number of hidden layers and neurons, activation functions, learning rate, and regularization. The hidden layers and neurons determine the network's complexity, activation functions control nonlinear mapping, the learning rate affects convergence speed, and regularization prevents overfitting (Shirmard et al., 2022).

## 2.5. Model Development and Performance Assessment

We used a grid search algorithm to ensure optimal performance of the models, aiding in the identification of the most suitable configuration for maximizing predictive accuracy (Maxwell et al., 2018; Varghese et al., 2023). Table 2 presents the search area for hyperparameter settings of each ML model. To train and test the ML models, we adopted a Leave-One-Out Cross-Validation (LOOCV) algorithm and two key statistical metrics, namely the coefficient of determination (R², Equation 1) and root mean squared error (RMSE, Equation 2).

$$R^2 = 1 - \frac{\text{RSS}}{\text{TSS}}, \qquad (1)$$

where RSS is the sum of squares of residuals and TSS is the total sum of squares.

$$\text{RMSE} = \sqrt{\frac{\sum_{i=1}^{N} (x_i - \hat{x})}{N}}, \qquad (2)$$

where *i* variable, *N* is the number of non-missing data points, *x*<sub>*i*</sub> are actual observations in the time series, and *hat*<sub>*i*</sub> is the estimated time series.

In LOOCV, 20% of the available data was held out exclusively for testing purposes. Subsequently, the remaining data underwent a four-fold cross-validation process, increasing the models' generalization capabilities (Maxwell et al., 2018). To further enhance the reliability and robustness of the results, the algorithm was rerun 10 times, and the results were averaged for assessment. We used the *Scikit-learn* python library to implement the grid search algorithm, ML models, and LOOCV algorithm (Pedregosa et al., 2011).

We further evaluated our model performance across diverse ecological and seasonal conditions, namely the International Geosphere Biosphere Programme (IGBP) land cover types (Sulla-Menashe et al., 2019) and month of the year (De Beurs & Henebry, 2005; Sulla-Menashe et al., 2019). To do so, we calculated a signal-to-noise ratio (SNR) for each month. SNR was defined as the mean GPP and RECO values divided by the mean absolute error (MAE), representing the clarity of the signal in the presence of noise. We then converted SNR to decibels (dB) units using multiplying the logarithm with base 10 by 10. Positive SNR values indicate that the signal is stronger than the noise, while negative SNR values suggest that the noise is stronger than the signal.
<figure><img src="image_5.png" /><figcaption>**Figure 3.** GPP and RECO estimation with four machine learning models using GOES-R surface reflectance products. (a) R² and (b) RMSE, based individual models trained on 2 years of data from 89 eddy covariance sites. The median is depicted on the box plots.</figcaption></figure>

In addition to evaluating model performance across diverse ecological and seasonal conditions, we also assessed the models' adaptability under different gap sizes to test their suitability for gap filling purposes (Gao et al., 2023; S. Zhu et al., 2022). We chose 12 gap lengths ranging from 1 hr to 1 year and randomly selected data from our available dataframe for each gap length from all sites. We then kept these data out of training for testing purposes and trained the ML model using the rest of the data. Lastly, we compared the diurnal dynamics of GPP estimated from ABI observation against partitioned from tower measurements. Given RECO's minimal diurnal variation, we only presented the model performance for GPP, and RECO results are presented in Supporting Information S1. For this analysis, we selected four AmeriFlux sites, focusing separately on GPP and RECO, ensuring that the chosen sites exhibited diurnal variability in the DOY during the year 2022 with minimum, median, and maximum values.

# 3. Results

## 3.1. Assessment of the Machine Learning Models Performance

Figure 3 shows the comparison of the GPP and RECO modeled using four ML algorithms by averaging the results of 10 runs of the LOOCV algorithm using surface products (i.e., BRFs and DSR) as inputs for ML models. As GOES-16 surface product data are only available starting in August 2021, we utilized data from August 2021 through December 2022 from a total of 89 sites with available eddy covariance observations. RF and GBR demonstrated higher performance for both GPP and RECO estimation with statistics presented in Table 3. MLP exhibited the worst performance, and SVR was not as effective as the tree-based learning models (Table 3).

With a mean (±standard deviation) R² value of 0.71 ± 0.11, and corresponding RMSE of 4.04 ± 1.65 µmol m⁻² s⁻¹, RF estimated GPP with slightly better fit than GBR, which had a mean R² of

<caption>**Table 3**</caption>

R² and RMSE for GPP and RECO Estimation Using 2 Years of Data From 89 Eddy Covariance Sites With Multiple ML Models (Mean ± Standard Deviation)

<table><thead><tr><th rowspan="3"></th><th colspan="4">GPP</th><th colspan="4">RECO</th></tr><tr><th colspan="2">Test</th><th colspan="2">Cross validation</th><th colspan="2">Test</th><th colspan="2">Cross validation</th></tr><tr><th>R<sup>2</sup></th><th>RMSE</th><th>R<sup>2</sup></th><th>RMSE</th><th>R<sup>2</sup></th><th>RMSE</th><th>R<sup>2</sup></th><th>RMSE</th></tr></thead><tbody><tr><td>RF</td><td><strong>0.71 ± 0.11</strong></td><td>4.04 ± 1.65</td><td>0.68 ± 0.12</td><td>3.28 ± 1.74</td><td><strong>0.77 ± 0.10</strong></td><td>0.90 ± 0.52</td><td>0.74 ± 0.10</td><td>0.77 ± 0.50</td></tr><tr><td>GBR</td><td><strong>0.70 ± 0.12</strong></td><td>4.15 ± 1.73</td><td>0.68 ± 0.13</td><td>3.34 ± 1.81</td><td><strong>0.77 ± 0.10</strong></td><td>0.90 ± 0.49</td><td>0.75 ± 0.11</td><td>0.76 ± 0.47</td></tr><tr><td>MLP</td><td>0.55 ± 0.15</td><td>5.23 ± 2.06</td><td>0.55 ± 0.15</td><td>4.20 ± 2.20</td><td>0.56 ± 0.16</td><td>1.42 ± 0.60</td><td>0.49 ± 0.20</td><td>1.20 ± 0.60</td></tr><tr><td>SVR</td><td>0.62 ± 0.14</td><td>4.70 ± 2.09</td><td>0.61 ± 0.13</td><td>3.81 ± 2.14</td><td>0.61 ± 0.15</td><td>1.18 ± 0.60</td><td>0.62 ± 0.14</td><td>0.99 ± 0.59</td></tr></tbody></table>

Note. Bold values highlight statistically significant findings or key metrics discussed in the text.
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Time series GPP estimated at AmeriFlux towers using Eddy Covariance (EC) measurements versus their corresponding GOES-estimated values using machine learning models on three DOY with minimum, median, and maximum GPP values at the AmeriFlux sites in the year 2022. The AmeriFlux sites are labeled in the top-left corner of the plots.</figcaption></figure>

0.70 ± 0.12, and a slightly higher RMSE of 4.15 ± 1.73 µmol m⁻² s⁻¹. For RECO estimation, similar trends emerged as observed in GPP estimation; RF and GBR continued to exhibit better performance with R² of 0.77 ± 0.10 and RMSE of 0.90 µmol m⁻² s⁻¹. Despite this equivalency in accuracy, GBR exhibited notable advantages in terms of speed, with faster model training and prediction times, as well as a smaller model size compared to RF (see Table S2 and Text S4 in Supporting Information S1).

Results from tests and cross-validation can assess model performance and generalization across data sets. All models exhibit close test and cross-validation results, indicating comparable predictive ability for both GPP and RECO. RF and GBR have slightly higher R² values in the test set (0.71 ± 0.11 and 0.70 ± 0.12) compared to cross-validation (0.68 ± 0.12 and 0.68 ± 0.13), suggesting an increased variance in predicting GPP for test data.

### 3.1.1. Different ML Models Response to Diurnal Variability

Figure 4 presents the results of assessing ML models for estimating diurnal GPP dynamics, comparing time series EC measurements with GOES-estimated values using the ML models in 3 days during 2022 at four AmeriFlux sites. The 3 days chosen for this analysis represent days with minimum, median, and maximum GPP values at the AmeriFlux sites in 2022. This selection ensures that the analysis captures the full range of GPP variability. Since RECO exhibits little diurnal variation (see Figure S2 in Supporting Information S1), we focused on evaluating the model's performance for GPP at the half-hourly scale. Findings for GPP indicate that, among the ML models, GBR and RF exhibit the highest agreement, while MLP displays the least. MLP and SVR consistently underestimate the maximum GPP values at AmeriFlux sites US-xTR and US-Rms, with more pronounced underestimation at US-Tw4. Additionally, MLP and SVR overestimate the minimum GPP values at US-Rms and US-Tw4. All models excel in estimating peak GPP values around 12 p.m. (local standard time) across the four sites, with GBR emerging as the most accurate model. For RECO, GBR, and RF also perform better than MLP
<figure><img src="image_7.png" /><figcaption>**Figure 5.** Feature importance from the selected GBR model trained on GPP estimates from 89 eddy covariance sites. Abbreviations are listed in Table 1.</figcaption></figure>

and SVR across all sites and for minimum, median, and maximum values (see Figure S2 in Supporting Information S1). Similar to GPP, MLP, and SVR consistently have underestimation and overestimation problems.

## 3.2. Feature Importance

NIRvP, NIRv, and BRF3 emerged as the most important features in the GBR model (Figure 5), followed by DSR and BRF6 (SWIR 2.2 µm). BRF5 (SWIR 1.6 µm) and BRF1 (Blue) were the least important features of those selected. DSR showed its importance after NIR-based features, even though NIRvP, the top-ranked feature, was derived using PAR, which is computed by DSR.

Further investigation for optimal model input led us to train and test three different GBR models using only BRF features, only CMI features, and a combination of BRF and CMI features separately. Comparing the results shows that the best feature combination for modeling GPP is the combination

of BRF and CMI, yielding an R² score of 0.69 ± 0.12 and an RMSE of 2.92 ± 1.27 µmol m⁻² s⁻¹ (Figure 6). The results for RECO are similar to those for GPP. The combination of BRF and CMI exhibits the best R² score of 0.74 ± 0.11 with a RMSE of 0.67 ± 0.31 µmol m⁻² s⁻¹.

When considering only surface reflectance, the most influential features are BRF3 (NIR), BRF2 (red), and BRF6 (SWIR). Conversely, when solely utilizing TOA observations, the prominent features become CMI_C3 (NIR), CMI_C12 (IR), and CMI_C6 (SWIR). Intriguingly, in the context of combined feature utilization, surface reflectance features retain their significance, outweighing TOA observations in importance for the three most important variables. However, it is noteworthy that TOA observations still maintain relatively high feature scores, with BRF3, BRF2, BRF6, CMI_C12, CMI_C3, and CMI_C6 emerging as the foremost informative features (feature importance figures are provided in Text S2 of the Supporting Information S1).

## 3.3. The Impacts of Longer Historical Records on Model Performance

Using the CMIs features, which extend back to 2017 from GOES-16, we evaluated the impact of longer data records on predicting GPP and RECO. Our analysis, depicted in Figure 7, shows that by extending the historical data for training the GBR model, we can consistently improve the predictive accuracy.

We considered historical data intervals of 1, 2, 3, and 4 years for training, ranging from 2020–2021 to 2017–2021. Comparing these models to those trained solely on 2021 data, we found that incorporating longer historical

<figure><img src="image_8.png" /><figcaption>**Figure 6.** A comparison of (a) R² and (b) RMSE for GPP and RECO estimation using up to 2 years of observations from 89 eddy covariance sites and the selected GBR model using different BRF and CMI feature configurations. The median is depicted on the boxes.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Figure 7.** Impact of historical GOES-R CMI data record length on GPP and RECO estimation accuracy using a GBR model trained on eddy covariance data from 126 sites: (a) ΔR² and (b) ΔRMSE differentiate the accuracy of models trained using 1, 2, 3, and 4 years more data than only 1 year data. The median is depicted on the boxes.</figcaption></figure>

records yielded a steady increase in accuracy. The R² increased by up to 0.09 ± 0.07, indicating better variance explanation, while the RMSE decreased by down to -0.11 ± 0.17 µmol m⁻² s⁻¹, highlighting improved accuracy.

### 3.4. Sensitivity of Models Performance to Seasonal Variations

For GPP estimations, July stands out as the month with the highest absolute uncertainty with a standard deviation of 3.30 µmol m⁻² s⁻¹. In contrast, January emerges as the month with the lowest absolute uncertainty, having a lower standard deviation of 1.22 µmol m⁻² s⁻¹. The SNR analysis reaches a peak in July (SNR = 8.13), signifying a relatively robust and reliable signal compared to January with the lowest SNR at 4.47, indicating a weaker signal relative to the background noise. Similarly, August is the month with the highest estimation error for RECO, with a standard deviation of 0.773 µmol m⁻² s⁻¹. Conversely, January emerges with lowest estimation errors, characterized by standard deviation of 0.296 µmol m⁻² s⁻¹. Similar to GPP, the SNR value for August was the highest at 9.53, indicating a notably stronger signal during this month compared to the lowest SNR of 6.94 observed in January.

Examining flux estimation across seasons reveals important insights into the model's performance. In winter, the standard deviation of GPP and RECO estimation errors is relatively low, at 1.31 and 0.299 µmol m⁻² s⁻¹, respectively (Figure S4 in Supporting Information S1). This increases in spring, indicating more variability in model estimations. During summer, the standard deviation peaks at 3.20 and 0.726 µmol m⁻² s⁻¹ for GPP and RECO, respectively. In autumn, the standard deviation decreases again. The mean SNR values for each season reflect this trend, with lower values in winter and spring, and higher values in summer and autumn for both GPP and RECO.

### 3.5. Sensitivity of Model Performance to Vegetation Type and Magnitude of GPP

Our analysis reveals notable variations in the model's performance for GPP estimation across different vegetation types (see Figure 9). Mixed Forests and Permanent Wetlands emerge as the top-performing categories, exhibiting a mean R² value of 0.78 for both. Conversely, Savannas display the lowest mean R² value, at 0.64, followed by Shrublands with an R² of 0.68 (Tables S3 and S4 in Supporting Information S1). Moving on to other vegetation categories, R² for GPP estimation is >0.72, where the lowest is for Evergreen Needleleaf.

Similar trends are observed in the model's performance for RECO estimation. Permanent Wetlands, Deciduous Broadleaf Forests, Croplands, Evergreen Needleleaf Forests, Grasslands, and Mixed Forests have the highest mean R² value of 0.80. On the contrary, Savannas exhibit the lowest mean R² value for RECO, at 0.69, pointing to relatively reduced accuracy but still predictive skill even in this challenging vegetation type. Similar to GPP estimation, Shrublands showed the lowest accuracy after Savannas in RECO estimation, with mean R² values of 0.73.

We further investigated the sensitivity of model performance with respect to the magnitude of GPP (Figure 10). The best GPP estimation was achieved for GPP values between 35 and 52 µmol m⁻² s⁻¹, followed by values
between 23 and 35 µmol m⁻² s⁻¹. The poorest performance was observed at sites with the lowest and very highest GPP (between 5–23 and more than 52 µmol m⁻² s⁻¹). For RECO estimation, a similar trend was observed; sites with intermediate values exhibited the best performance, while those with the highest and lowest biomass demonstrated lower R² values.

### 3.6. Models Performance for Gap Filling

The accuracy of our GBR model for gap filling over various gap lengths reveals variations in accuracy relative to gap length (Figure 11). As expected, the 1-, 2-, and 4-hr intervals exhibit the highest mean R² of 0.70 for GPP and 0.75 for RECO (Tables S5 and S6 in Supporting Information S1) and gap sizes greater than 1 week exhibit the lowest mean R².

Figure 11 shows that accuracy gradually decreases as the gap length increases beyond 1 day, with a more pronounced decline observed after the 3-day interval. However, according to GPP results, the maximum R² values of 0.82 and 0.81 for 6-month and 1-year intervals, respectively, demonstrate that the model maintained accuracy, even for longer gap sizes. The same trends are observed for RECO, with maximum values of R² of 0.86 and 0.79 for 6-month and 1-year intervals (Tables S5 and S6 in Supporting Information S1).

## 4. Discussion

### 4.1. Performance of the Machine Learning Models

Our analysis of ML models with ABI data reveals that ensemble methods like RF and GBR outperform individual models like MLP and SVR. This underscores the need for ensemble techniques to capture complex relationships between surface-atmosphere fluxes and GOES-R data, aligning with past research favoring ensemble models in remote sensing applications (T. Yu et al., 2021; Y. Zhang et al., 2022). For example, T. Yu et al. (2021) compared five ML models, including RF, SVR and ANN, to upscale GPP using MODIS observations. Their results highlighted the superiority of RF, achieving the highest accuracy (R² of 0.86 and RMSE of 0.99 g C m⁻² d⁻¹) compared to the other models. Additionally, X. Li et al. (2023) used a Cubist regression tree model with GOES-R data to predict daily GPP variations during the 2020 U.S. heatwave. They found the regression tree effective in forecasting carbon-related variables, emphasizing the importance of multiple factors in GPP estimation. This improvement is likely due to ensemble learning's ability to merge predictions from multiple models, reducing variance and enhancing accuracy (Sagi & Rokach, 2018; Y. Zhang et al., 2022).

In ensemble models like RF and GBR, decision trees are used for predictions. GBR stands out for employing a boosting algorithm to refine predictions. Ensemble models excel at managing missing data and noise (Kursa, 2014), common in ecological data sets (e.g., Moffat et al., 2007). RF and GBR handle missing data by assigning weights to observed variable values' frequencies based on their proximity. This technique benefits from post-training information from the mean-imputed data set (Ezzine & Benhlima, 2018). These learners are skilled at managing noisy data (Elbeltagi et al., 2023) by averaging estimations from multiple decision trees, reducing the impact of noise on overall performance (Jun, 2021).

Comparing ML approaches for time series analysis of diurnal GPP and RECO dynamics reveals similar trends. GBR and RF consistently exhibit the highest agreement, while MLP shows the least agreement, suggesting limitations in its ability to predict diurnal carbon flux. Notably, MLP and SVR consistently underestimate maximum values and overestimate minimum values for both GPP and RECO, which is a common challenge in ML modeling when estimating small and large values in the trained data frame (Belitz & Stackelberg, 2021; Ye et al., 2021). Despite these nuances, all models excel in accurately estimating peak GPP values around 12 p.m., with GBR emerging as the most precise. However, all models demonstrate fluctuations in RECO estimation, attributed to the use of highly dynamic satellite observations as modeling inputs and the limited variability of tower estimated values through partitioning EC measurements (see Figure S2 in Supporting Information S1).

GBR demonstrates performance similar to RF but with a smaller digital model size and faster prediction time, making it favorable for data-intensive geostationary workflows (Table S2 in Supporting Information S1). Although GBR models require more construction time than RF models due to tuning additional hyperparameters and the sequential building process (Golden et al., 2019), they often exhibit superior predictive capabilities (Golden et al., 2019; Shen & Yong, 2021; Y. Zhang et al., 2022). By optimizing a reduced set of predictors and the
model's maximum depth, GBR achieves comparable predictive accuracy to RF models but with fewer trees and maximum depths (Y. Zhang et al., 2022), leading to expedited training and a more compact digital model size.

## 4.2. Feature Importance

The feature importance analysis in this study highlights NIRvP, NIRv, and BRF3 as crucial factors influencing GPP and RECO estimation using GOES-R measurements and data products (Khan et al., 2022; Todd & Hoffer, 1998). These features, all derived from the NIR spectral band, underscore the significant role of vegetation reflectance in carbon exchange processes within ecosystems (Khan et al., 2022; Richardson et al., 2013). Baldocchi et al. (2020) further demonstrate a robust correlation between reflected near-infrared radiation and canopy photosynthesis, emphasizing the effectiveness of near-infrared radiation as a cost-effective tool for monitoring carbon assimilation across various conditions and time scales. Following these features, DSR is vital in estimating GPP and RECO as it serves as a proxy for PAR—the solar radiation essential for photosynthesis (Dechant et al., 2022; Khan et al., 2022; Wohlfahrt et al., 2010). DSR allows assessment of available light energy, positively impacting GPP when stressors are absent, enabling plants to harness more energy for photosynthesis (Bosiö et al., 2014; Oliphant et al., 2011). Although the relationship between PAR and RECO is more nuanced, it is influenced by GPP, which, in turn, is driven by PAR (Austin & Vivanco, 2006; Jia et al., 2020).

After DSR, SWIR2 at 2.2 µm (BRF6) stands out as a crucial feature, emphasizing the importance of shortwave irradiance and spectral bands for vegetation functioning. The 2.2 µm channel aids atmospheric correction, reducing noise and atmospheric interference (Hese et al., 2005). SWIR-VIs, particularly when creating VIs using SWIR and NIR bands, perform comparably to VNIR-VIs (creating VIs using visible and NIR bands) and excel in certain cases for vegetation monitoring (Benseghir & Bachari, 2021; Massetti et al., 2019). For instance, SWIR-VIs explain 40% more variability than VNIR-VIs in aboveground biomass estimation (Benseghir & Bachari, 2021). SWIR-VIs have also been used for understanding the impacts of snow, phenological change, and water availability on terrestrial ecosystem functioning. Whereas the particular mechanism by which the GBR model identifies BRF6 as an important input is difficult to ascertain from ML approaches, and a combination of the above factors is likely the cause of its importance at ecosystems distributed across CONUS, our findings suggest that incorporating SWIR bands could significantly improve the accuracy of geostationary satellite-based carbon cycle products.

Three distinct GBR models constructed by different combinations of TOA and BRF features shows that the combination of both BRF and CMI features demonstrates superior performance over models utilizing either TOA products or surface reflectances alone. However, we acknowledge the complexity of converting TOA to surface reflectance, especially when correcting for BRDF effects, which presents significant challenges in accurately capturing surface properties. Achieving good sub-daily estimations of GPP and respiration using only TOA observations demonstrates the potential for simpler and more efficient remote sensing methods. CMIs show comparable performance to BRFs, highlighting their potential as an alternative data source in carbon cycle science. While TOA information is less utilized in vegetation science (Bayat et al., 2020; Kovács et al., 2023; Parente, 2013; Yuanyong & Shenghui, 2013), we argue that it holds promise for enhancing geostationary satellite-based carbon cycle products. For training and testing, our study used filtered data to exclude low-quality data using ABI quality flags. Further research is needed to evaluate the utility of TOA observations under various data quality conditions, including cloudiness and optically thick aerosol atmospheres. The analysis highlighted the importance of surface reflectance features, even when combined with TOA observations, while the thermal infrared band CMI_C12 also showed significance (see Figure S3 in Supporting Information S1), warranting further investigation. While the specific reason for CMI_C12's explanatory power in machine learning models remains unclear, it is discussed in more detail in Supporting Information S1 (see Text S2).

## 4.3. Longer Historical Record Effects on Model Performance

By assessing the influence of extending historical data records on the estimation accuracy of GPP and RECO, we have demonstrated that extended historical data better explains their variance. Several studies have shown the positive impact of longer data records on model performance in ecological and environmental modeling (Han et al., 2023; Houghton et al., 1983; Nemani et al., 2003). Furthermore, historical remote sensing and ground-based data improve predictive power for greenhouse gasses and provide historical flux data, establishing reference emission levels adjusted for national circumstances (COP15—Copenhagen, 2009). This enhanced understanding
can contribute to more informed decision-making and policy formulation regarding carbon sequestration, ecosystem health, and climate resilience (Anderegg et al., 2020; COP15—Copenhagen, 2009; Millar et al., 2007).

## 4.4. Sensitivity of the Model Performance to Seasonal Variability

Assessment of model performance to seasonality offers valuable insights into the model's accuracy and consistency across different seasons, which align with established ecological patterns. July, characterized by the highest standard deviation in error estimations and SNR for GPP, corresponds to the peak of Northern Hemisphere summer when factors such climate and weather anomalies including temperature, water stress, precipitation, and photosynthesis complicate accurate estimation of GPP from satellite remote sensing (Felton & Goldsmith, 2023; Wei et al., 2017). In contrast, the lowest estimation errors and SNR values in winter are likely a result of reduced environmental variability and near-zero photosynthesis in many ecosystems during this season (Irisarri et al., 2012; Qiu et al., 2020). The contrast between high summer SNR and low winter SNR values suggests that the discernibility of the carbon flux signal diminishes in winter due to environmental factors. This makes winter data less reliable compared to summer. Transitioning to spring, there is a noticeable surge in variability in estimation errors compare to winter, which may be attributed to the onset of new growth and changing environmental conditions (Weltzin et al., 2020), and result in spatial heterogeneity in carbon fluxes at fine spatial scales that challenge the relatively coarse pixels of geostationary satellites (Khan et al., 2022).

Similar to GPP, RECO estimations exhibit seasonal variations. The highest estimation errors and SNR values during summer months can potentially be explained by the intricate interplay of temperature, moisture, and plant activity in this season (Felton & Goldsmith, 2023; Gong et al., 2022; Younes et al., 2020). During winter, RECO estimates exhibit lower errors and SNR values (Figure 8), reflecting the impact of reduced environmental variability (at least under snow) and, often, near-zero photosynthesis to which RECO is coupled. These lower errors are attributed to smaller RECO values and reduced standard deviation during winter months (Gong et al., 2022; Irisarri et al., 2012; Wei et al., 2017). The increase in variability during spring compared to winter (Figure 8) aligns with the transition period when vegetation begins to emerge across much of the study domain, resulting in rapid increases in both GPP and RECO. This growth and heightened photosynthetic activity also contribute to an increase in SNR values, indicating stronger signals over background noise for carbon flux data in spring (Gong et al., 2022; Younes et al., 2020). These findings underscore the importance of seasonality in model skill, requiring further research and model refinement during variable periods (Gong et al., 2022; Wei et al., 2017; White & Hastings, 2020).

## 4.5. Sensitivity of the Model Performance to Vegetation Type and Magnitude of GPP

Our analysis of the GBR model's performance across various vegetation types has revealed variations in the accuracy of GPP and RECO estimation (Figure 9). Mixed Forests and Permanent Wetlands consistently stand out as top-performing categories, while Savannas and Shrublands exhibit comparatively lower predictability. This differential performance is critical to understanding the model's adaptability within diverse ecological settings.

The challenges faced in predicting GPP and RECO in Savannas and Shrublands arise from their inherent complexity and environmental variability. Savannas encompass a mix of grasses and scattered trees, resulting in intricate and fluctuating carbon exchange dynamics that are challenging to model accurately (Littlefield & D'Amato, 2022; Mistry, 2000). Similarly, Shrublands comprise a mix of shrubs and grasses, leading to high spatial and temporal variability in carbon exchange processes which also makes them challenging to model (Y. Li et al., 2021). Moreover, the reduced estimation accuracy within Shrublands and Savannas can be attributed in part to the sub-pixel variability in green-up (Gessner et al., 2013; X. Ma et al., 2013), resulting in fluctuating NIR reflectance and challenges in remote sensing modeling.

Deciduous Broadleaf Forests, Croplands, Evergreen Needleleaf Forests, and Grasslands demonstrate a similar degree of predictability, the model's performance varies across these vegetation types. The forested vegetation types represent diverse ecosystems with distinct physiological characteristics (S. Zhu et al., 2023) that the GBR model appears to simulate well in Deciduous Broadleaf Forests, with poorer average fit in Evergreen Needleleaf Forests that consistently challenge remote sensing modeling of their carbon cycle (Liu et al., 2016).

The variations in model performance among these vegetation types—when using satellite data products alone—can be speculated to a combination of factors such as environmental conditions, vegetation structure, and
<figure><img src="image_10.png" /><figcaption>**Figure 8.** The monthly variability in estimation errors and signal-noise ratio (SNR) for (a) GPP and (b) RECO grouped by month from the GBR model.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>**Figure 9.** Performance of the GBR model trained on data from 89 eddy covariance sites for (a) GPP and (b) RECO estimation, employing a combination of BRF and CMI features across various IGBP vegetation types.</figcaption></figure>

ecological dynamics. Deciduous Broadleaf Forests often exhibit well-defined seasonal patterns of carbon exchange, making them more predictable in this regard (Muraoka, 2022) but noting that phenological transitions remain difficult to model (Richardson et al., 2013). Evergreen Needleleaf Forests have more subtle changes in greenness and canopy structure, Grasslands may have different ecological dynamics including rapid recovery from disturbances and fine-scale heterogeneity (Novick et al., 2004), which can be difficult to estimate from space (W. Chen et al., 2023; Qiu et al., 2020).

## 4.6. The Model Performance for Gap Filling Missing Eddy Covariance Data

The eddy covariance method is the main monitoring tool for measuring the net ecosystem exchange (NEE), defined as the net flux of CO₂, balancing ecosystem respiration (release, RECO) and photosynthesis (uptake, GPP) (Gao et al., 2023; S. Zhu et al., 2022). These measurements are reported on a half-hourly or hourly basis. However, calibrations, equipment failures, and low turbulence conditions often result in gaps in these data series, with 20%–60% or more of annual data typically missing (Irvin et al., 2021; Moffat et al., 2007; S. Zhu et al., 2022). Testing the models’ adaptability under different gap sizes is crucial for ensuring the reliability and accuracy of gap-filling techniques. Continuous NEE data sets are essential for estimating ecosystem carbon budgets, evaluating process model predictions, and comparing with biometric measurements (Falge et al., 2001). In the domain of strategies for filling gaps in C-cycle flux time series, a diverse array of approaches including non-linear and linear regression techniques, semi-parametric models, marginal distribution sampling, look-up tables, and the application of ML models has been widely utilized (Irvin et al., 2021; Kim et al., 2020; Moffat et al., 2007; S. Zhu et al., 2022). Nevertheless, a nuanced comparison between these approaches showed that ML models, including
<figure><img src="image_12.png" /><figcaption>**Figure 10.** Performance of the GBR model trained on data from 89 eddy covariance sites for (a) GPP and (b) RECO estimation across different GPP bins.</figcaption></figure>

NNs, RF, and SVR, highlight their superiority over alternative approaches (Gao et al., 2023; Irvin et al., 2021; Kim et al., 2020). This study also investigates the potential of the GBR model for gap-filling purposes. The model exhibits higher accuracy over shorter gap lengths (e.g., 1 hr–3 days), benefiting from more complete training data for three consecutive days (Figure 11), which facilitates effective learning (S. Chen et al., 2018; Maxwell et al., 2018). Shorter intervals provide less fragmented data, enhancing the GBR model’s ability to capture short-term variations and dependencies. The high level of accuracy achieved for gap sizes of up to 1 day is promising (Figure 11, and Tables S5 and S6 in Supporting Information S1 shows R² of 0.75 for RECO and 0.7 for GPP), as it demonstrates the model’s capability to effectively fill approximately 99% (refer to Figure S1 and Text S3 in Supporting Information S1) of the gaps in time series data at the site-level observation, as depicted in Figure 11. Nevertheless, the model maintains acceptable performance in longer gap lengths, with an R² of more than 0.5 for gaps up to 6 months and 1 year. The maximum observed R² for longer gap lengths (Tables S5 and S6 in Supporting Information S1), including those up to 1 year, was more than 0.85 for GPP and RECO at some sites, under-scoring the ability of geostationary-based carbon cycle estimates to fill extended data gaps alone, in the absence of micrometeorological data inputs.

Several methods offer distinct advantages and trade-offs for carbon flux observation gap filling (LeCun et al., 2015; Moffat et al., 2007). Simple interpolation like mean diurnal variation have the potential for bias and even more advanced techniques like the dual unscented Kalman filter, multiple imputation, and terrestrial biosphere models still exhibit significant biases in gap filling, lowering reliability (Moffat et al., 2007; Vekuri et al., 2023). ML models like the GBR exhibit higher accuracy, particularly in capturing short-term variations and dependencies, without overly taxing computational resources. While deep ML models like deep NNs are powerful (LeCun et al., 2015; Yuan et al., 2020), they require substantial data and computational resources

<figure><img src="image_13.png" /><figcaption>**Figure 11.** The GBR model performance for (a) GPP and (b) RECO gap filling for different gap lengths.</figcaption></figure>
(Guicquero & Sicard, 2022; Simran et al., 2020; Yuan et al., 2020). Physical methods offer accuracy but demand extensive parameterization and domain-specific knowledge (Moffat et al., 2007; X. Zhu, 2014). The GBR model, known for its versatility, strikes a balance between accuracy and efficiency (Sagi & Rokach, 2018; Sahin, 2020), making it arguably suitable for both short and long gaps in GPP and RECO data across various ecosystem types.

## 4.7. Comparing GPP and RECO Predictability Using GOES-R Data

Although both GPP and RECO were estimated with an R² of more than 0.70 by the GBR model, RECO estimates were more accurate (Table 3); the RMSE of RECO (0.90 µmol m⁻² s⁻¹) was significantly lower than the RMSE of GPP (4.15 µmol m⁻² s⁻¹). The discrepancy in the RMSE between GPP and RECO estimations obtained through the GBR can be attributed to two primary factors.

First, the superior estimation of RECO over GPP using GOES-R satellite data underscores the advantage of employing the spectral bands to capture essential facets of carbon cycling. The blue and red bands are sensitive to vegetation chlorophyll and leaf structural properties, providing valuable insights into photosynthesis processes (Gitelson et al., 2014; Magney et al., 2017). The NIR and SWIR bands, on the other hand, offer critical information on vegetation and ecosystem water content and canopy structure, factors closely linked to respiration (RECO) (Lees et al., 2018; M. Wang et al., 2020). Moreover, in ecosystems where heterotrophic respiration significantly contributes to the carbon budget, like forested environments with relatively high soil respiration rates (Harmon et al., 2011; Mayer et al., 2017), the SWIR band's capacity to monitor soil moisture (Tian & Philpot, 2015) and temperature (Yang et al., 2011) may become particularly advantageous. These features are consistent with the drivers of RECO, which is dependent on photosynthesis, soil and vegetation water content, and canopy structure in addition to temperature (Lei et al., 2018).

Second, the broader range of actual values for GPP (8.72 ± 9.14 µmol m⁻² s⁻¹) in comparison to RECO (3.11 ± 2.73 µmol m⁻² s⁻¹) leads to a larger RMSE for GPP, as RMSE gauges the average magnitude of estimation errors relative to the actual values. To provide a more equitable basis for comparison, we can explore the normalized RMSE (nRMSE) by dividing the RMSE values by the respective mean values for GPP and RECO. This normalization allowed us to consider the scale differences and, thus, revealed that the nRMSE for GPP (0.46) and RECO (0.30) reflects their relative accuracy after accounting for the variable ranges. This finding is reinforced by our comparison of SNR values between GPP and RECO. RECO consistently exhibits higher values, indicating superior signal-to-noise performance compared to GPP. Lastly, the higher R² values in estimating RECO indicate superior performance compared to GPP (Table 3), similar to the difference in nRMSE between RECO and GPP.

## 4.8. Strengths, Limitations, Suggestions, and Opportunities

GOES-R data offer several strengths that bolster their utility in ecological modeling (Khan et al., 2021; Losos et al., 2024). They provide “hypertemporal” coverage of 5–10 min or less across key domains, and together with other geostationary satellites continuously monitor much of the Earth’s surface. This extensive coverage enhances our ability to capture high temporal ecological processes over a wide geographic area. Furthermore, the multispectral nature of GOES-R observations provides diverse information from surface and atmospheric characteristics synchronized over time and space, making them valuable inputs for ML modeling (Khan et al., 2021, 2022; X. Li et al., 2023). GOES-R data integration with ML models has shown promise in estimating GPP and RECO, highlighting the potential of satellite-derived information in ecological research (X. Li et al., 2023). Despite these strengths, the coarse spatial resolution of GOES-R data can be a constraint, particularly when analyzing fine-scale ecological phenomena. Moreover, GOES-R satellites collect data at off-nadir angles for most areas in its scene, leading to angular distortions in observations, requiring careful preprocessing and quality control before using the data (Khan et al., 2022; Losos et al., 2024). Therefore, to further leverage GOES-R data, future research can explore spatial downscaling techniques and data fusion (J. Li et al., 2022; L. Li et al., 2022; Schmitt & Zhu, 2016; J. Zhang, 2010) to enhance their resolution for more localized ecological modeling. While the modeling approach from this study holds great potential for regional mapping and applications such as near real-time carbon flux monitoring, exploring these applications is beyond the scope of this paper.

Eddy covariance data serve as robust validation tools for ecological models, offering the best available ground-truth information. These measurements are collected from (often) strategically placed sites representing various ecosystems and provide accurate and continuous records (Running et al., 1999). However, limited spatial
coverage and temporal gaps in the data can constrain their global-scale applicability. Therefore, utilizing ML and/or satellite data to fill temporal gaps is an important research problem which has been widely investigated (Irvin et al., 2021; Kim et al., 2020; Moffat et al., 2007). Furthermore, various global GPP products exist, such as MODIS with an 8-day temporal resolution and 1 km spatial resolution or FluxCom with higher temporal resolution and a coarser spatial resolution (0.5°–1°) (Jung et al., 2019; Sun et al., 2019). This study demonstrates the accurate modeling of GPP and RECO at the site level using observations from the geostationary satellite GOES-R ABI and ML models. However, the absence of global flux products from geostationary satellites poses a significant research challenge. Developing ML models for global flux maps utilizing remote sensing geostationary satellite data can effectively address the scalability challenge in carbon cycle modeling, enabling the creation of frequent (up to half-hourly) planetary-scale maps (Ai et al., 2020; Pabon-Moreno et al., 2022; Turner et al., 2006; T. Yu et al., 2021).

Unlike our study's approach, global map generation combines data from all sites rather than treating each site separately. Incorporating site-specific data in generating global maps improves model interpretability and relevance by addressing local variations in factors like land use, vegetation, climate, elevation, and location. Global maps also identify regions with high uncertainty in carbon flux dynamics, potentially requiring additional monitoring. However, it may pose challenges in terms of generalizability to ecosystems underrepresented in the site database and variations in data quality. For example, if the number of sites in one dominant climate or ecosystem vastly outweighs those in other areas, the map generality for less-represented regions becomes difficult to achieve, potentially leading to overfitting of the machine learning model in that particular area. Conversely, if there's a significant lack of data in specific regions, it can introduce bias into the global maps, affecting the accuracy of estimations in those underrepresented areas.

We note that other ML models not used in our study have demonstrated promising performance for monitoring complex ecological processes, including deep learning (Nathaniel et al., 2023; Yuan et al., 2020), recurrent neural networks (Nathaniel et al., 2023; Wong et al., 2023), and Gaussian process regression (R. Ma et al., 2022), which can improve estimation accuracy. For instance, Long Short-Term Memory (LSTM) is a well-known recurrent neural network used for modeling time series data due to its potential to capture complex patterns (Hochreiter & Schmidhuber, 1997; Talib et al., 2021), making it well-suited for tasks that involve capturing temporal relationships and nuanced dependencies within the data (Reddy & Prasad, 2018). However, because LSTM is computationally expensive (Guicquero & Sicard, 2022; Simran et al., 2020; Zeng et al., 2020), we opted for MLP among NN models given its lower computational requirements, while acknowledging the potential of LSTM and other deep learning approaches for future exploration.

## 5. Conclusion

Our study demonstrates the potential of leveraging surface reflectance products (BRF) and top-of-atmosphere products (TOA) data from the geostationary satellite GOES-R to estimate GPP and RECO across 126 Ameri-Flux sites at sub-daily scales. Gradient boosting regression (GBR) model proves effective in this estimation. Notably, surface reflectance features, especially those in the NIR spectral band, play a crucial role, while visible, SWIR, and IR reflectances, as well as radiances, further enhance performance. Combining TOA products with surface reflectance improves estimation, with longer historical records proving valuable in capturing temporal patterns. Moving forward, we envision an exciting path for further advancements in carbon flux estimation and ecosystem understanding using GOES-R and other geostationary satellite observations. Integrating meteorological and site-specific data with satellite imagery will help account for local environmental variations to construct near-global scale machine learning models. This expansion beyond site-level modeling promises a more comprehensive understanding of carbon flux dynamics. We recommend diversifying modeling approaches to further enhance estimation accuracy and accommodate temporal dependencies, which could involve integrating computationally expensive deep learning techniques such as Long Short-Term Memory (LSTM). Accurate prediction of GPP and RECO informs more robust ecological models, deepening our understanding of the planet's carbon cycle and its environmental implications.

## Conflict of Interest

The authors declare no conflicts of interest relevant to this study.
Acknowledgments

We thank eddy covariance tower principle investigators and research teams for collecting eddy covariance data and the Dept of Energy Ameriflux Network Management Project and the National Science Foundation supported National Ecological Observatory Network (NEON) network for organizing eddy covariance data and providing quality checks, hosting, and maintaining the eddy covariance databases. We likewise thank the GOES-R data providers. Support for this research was provided by the University of Wisconsin—Madison Office of the Vice Chancellor for Research and Graduate Education with funding from the Wisconsin Alumni Research Foundation, the U.S. National Science Foundation Macrosystems Biology award 2106012, and the USDA Hatch program. Dr. Bethany Blakeley, Dr. Ankur Desai, Dr. Hamid Ebrahimy, Dr. Jason Otkin, Ojaswee Shrestha, and Dr. Zhou Zhang provided critical insights and support. We thank the editors and the three anonymous reviewers for their valuable feedback, which has improved the quality of this manuscript, and for their dedicated service to the academic community.

Data Availability Statement

All modeling code and results plot are available at https://doi.org/10.5281/zenodo.12537040 (sranjbar94, 2024).

All GOES-R (Losos et al., 2024) and Ameriflux data used here are public and open-access.

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