Photosynthesis represents the single largest CO2 flux between the atmosphere and the biosphere. At the canopy level, the sum of all leaf photosynthesis is termed gross primary productivity (GPP), and accurate characterization of GPP represents a major uncertainty in the carbon cycle . Directly measuring GPP from remote sensing systems (e.g., satellites) is not presently possible. Instead, previous work has utilized stationary measurements of net ecosystem exchange (NEE) from flux towers that can be decomposed into GPP and respiration e.g.,. Observable quantities from satellites (e.g., vegetation indices computed from reflectance data) are then related to GPP inferred from flux towers e.g., in light use efficiency (LUE) or machine learning models to derive global estimates of GPP.

Vegetation indices such as the normalized difference vegetation index (NDVI) and near-infrared reflectance of terrestrial vegetation (NIRv) combine two (or more) spectral bands with different absorption characteristics to infer quantities related to plant physiology and canopy structure. The MODIS instrument was launched on the Terra and Aqua satellites in 1999 and 2002, respectively. This instrument has proved particularly useful due, in part, to the long operational lifetime, and vegetation indices can be derived from the individual reflectance bands of MODIS. More recently launched satellites, like Sentinel-5P, carry instruments with the necessary signal-to-noise ratio and spectral resolution to retrieve solar-induced chlorophyll fluorescence (SIF). The electromagnetic signal SIF is emitted by chlorophylls during photosynthesis. SIF is emitted in the red–far-red wavelengths of 650–850 nm . It is a way, besides photochemistry and nonphotochemical quenching, for de-excitement of the chlorophylls . Even though the link between chlorophyll fluorescence and photosynthesis is nonlinear at leaf and canopy scale, that does not hold for satellite scales, in which a linear relationship of SIF to GPP is frequently reported e.g.,.

Vegetation indices (also termed greenness) can be regarded as a measure of photosynthetic capacity , whereas SIF indicates photosynthetic activity. SIF has been shown to be a powerful proxy for estimating GPP , to capture the impact of drought on photosynthetic activities across different vegetation types , and to assess the regional source of carbon emissions .

described the first retrievals of SIF from the TROPOspheric Monitoring Instrument (TROPOMI), the sole instrument on the Sentinel-5P satellite. The TROPOMI instrument has an equatorial crossing time of 13:30 local solar time and a 16 d orbit cycle. TROPOMI has a wide swath (2600 km across track) that allows for near-daily temporal resolution and a spatial resolution of 5.5 × 3.5 km. This was a substantial improvement to previous satellite instruments measuring SIF that were limited to 40 × 40 km spatial resolution . Despite the higher spatial resolution of TROPOMI, there have been efforts to estimate SIF at finer spatial scales e.g.,. This is motivated by the importance of fine-scale phenomena in the carbon cycle such as ecosystem fragmentation e.g.,.

Globally, 20 % and 70 % of the remaining forests are within a distance of 100 m and 1 km, respectively, from the forest edges, meaning that most of the forests are fragmented . show that the carbon uptake and storage of trees near the forest edge increase up to 13 ± 3 % and 10 ± 1 %, respectively. On the other hand most of our understanding about forest carbon fluxes comes from intact ecosystems, resulting in a mismatch between the ecosystems we are trying to quantify and the data we are using to do so . Higher-resolution estimates of photosynthetic activity might enable us to include fragmentation effects of ecosystems to global carbon cycle estimates or biosphere models like , , and . Additionally, recent work has shown the importance of fine-scale variations in the urban biosphere on the overall carbon flux for a city e.g.,.

There has been some recent work with the goal of increasing the resolution of existing global SIF estimates through downscaling methods (i.e., physics-based methods). For example, used NIRv to partition SIF within a particular TROPOMI scene and oversampled it using a 16 d window afterwards, resulting in a daily 500 m SIF estimate over the conterminous United States (CONUS). downscaled GOME-2 satellite SIF from 0.5 to 0.05∘ using a parameterization with a term for the fraction of absorbed photosynthetically active radiation (fAPAR), one for water stress, and one for heat stress based on MODIS data. used airborne data to downscale far-red SIF from canopy to leaf level.

Machine learning has also been used to create global, high-resolution SIF data sets. , , and used spectral bands from MODIS as input to neural networks that were trained with Orbiting Carbon Observatory 2 (OCO-2) SIF data to build global continuous SIF products at 0.05∘ resolution. OCO-2 has a narrow swath, and, therefore, the networks are trained only in the regions where OCO-2 SIF is available by using MODIS data as input. After training, the global MODIS data are used as input to estimate SIF on a global scale. use MODIS reflectance data as input and predict GOME-2 normalized by clear-sky irradiance. Multiplying that with a MODIS-derived photosynthetic active radiation product results in a MODIS only estimated SIF, termed RSIF. trained a convolutional neural network (CNN) with MODIS data on the artificial GOSIF data set at a resolution of 0.05∘ and used the trained network and MODIS data at a resolution of 0.008∘ to estimate SIF at 0.008∘. The physics-based downscaling approach from can only consider one variable for weighting the SIF signal, while the machine-learning-based approaches in the literature can consider more than one variable – but many do not use SIF data as an input to their model, meaning that they estimate SIF based on reflectance data.

Here we build a convolutional neural network to obtain high-resolution SIF, named SIFnet. SIFnet increases the spatial resolution of TROPOMI SIF by considering coarse-resolution SIF with high-resolution auxiliary data as input. These auxiliary data consist of either proxies of SIF or photosynthetic drivers. SIFnet is trained using data with near-global coverage. Different model parameters (structure, input features, and scaling factors) are compared and evaluated. After training the model, the resolution of TROPOMI SIF is refined by a factor of 10 to a spatial resolution of 0.005∘. This product is then compared against a recent downscaling method from the literature . Both high-resolution estimates are validated over CONUS against the independent SIF measurements of the OCO-2 and OCO-3 instruments (together OCO-2/3) .

The differences in SIF predicted from SIFnet and the downscaled SIF beg the question: which is correct? Here we evaluate both SIF products against independent SIF observations from OCO-2 and OCO-3 . These instruments have higher spatial resolution than TROPOMI and, as such, can be used to evaluate the high-resolution patterns predicted by both SIFnet and the downscaling approach. Specifically, OCO-2 and OCO-3 have nadir footprint sizes of 2.25 × 1.29 km. However, OCO-2 and OCO-3 do not provide full spatial coverage. They observe narrow swaths that are ∼ 10 km across-track. OCO-3 also provides a scanning mode to observe urban areas. Here, we use quality-checked OCO-2 data from April 2018 until March 2021 and OCO-3 data from July 2019 until March 2021. To compare the ungridded OCO-2 and OCO-3 data against the SIF estimated from TROPOMI, we compute the weighted average of all 0.005∘ grid cells that fall within the bounds of an OCO footprint. Here, the TROPOMI estimates are subsampled to 0.0005∘ (approx. 50 m at Equator), and the mean value is computed for all values which fall into the OCO footprint. For a quantitative comparison between OCO-2/3 and the SIFnet and downscaled estimate, the metrics r, r2, and RMSE are computed.

The high-resolution SIF estimates from SIFnet, the downscaling, and OCO are instantaneous SIF measurements taken at a specific time of the day, while the time of TROPOMI observations can differ substantially. Here, we compute the daily average SIF by scaling with the cosine of the SZA : 6DailySIF(x,y)=SIF(τs,x,y)⋅∫τoτfcos⁡[SZA(τ,x,y)]dτcos⁡[SZA(τs,x,y)], where DailySIF(x,y) is the daily integrated SIF estimate, SIF(τs,x,y) is the instantaneous SIF at the individual measurement time, SZA is the solar zenith angle, τs is the time of the satellite measurement, τo is the time of sunrise, and τf is the time of sunset. This implicitly assumes that both PAR and SIF scale with cos⁡(SZA) under cloud-free conditions, and we neglect Rayleigh scattering, as well as gas absorption. Although this approach neglects several water or light conditions, it provides our best estimate of daily SIF and enables comparison between multiple SIF products with different measurement times . The method is equivalent to the daily correction scheme for OCO-2, OCO-3, and TROPOMI . Additionally, we performed a sensitivity study in which we trained SIFnet using daily corrected SIF and found the results to be generally insensitive to the use of instantaneous vs. daily corrected SIF (see Fig. S18). Following this, we chose to apply the daily correction after deriving the high-resolution SIF.

Figure  shows a comparison of both SIFnet and the downscaled SIF to OCO-2 and OCO-3. Specifically, Fig. a shows the correlation of SIFnet and the downscaled estimate with OCO-2/3 for every 1∘ pixel over CONUS. Both SIFnet and the downscaled SIF generally show good agreement with r in excess of 0.7 for most of the high-productivity regions. We observe weaker correlations in the western drylands due, in part, to a lower signal-to-noise ratio. Overall, we find SIFnet to perform systematically better than the downscaled SIF, as shown in the difference plot. Figure b summarizes these spatial patterns in a scatterplot comparison. SIFnet again shows better performance than the downscaled SIF against OCO-2, OCO-3, and OCO-2/3. The Pearson correlation coefficient r is 0.78 and 0.72 for the SIFnet and downscaled estimate, respectively, when comparing to all OCO data (right column in Fig. b). The generally high RMSE indicates different scales and variability in the data sets.

Deviations between TROPOMI and OCO-2/3 also appear at a grid of 0.05∘ (Fig. S19). The r2 coefficient is 0.61 and 0.62 between TROPOMI and OCO-2 and OCO-3 SIF, respectively. Indeed, one might expect better correlations here as both present SIF at 740 nm. However, as pointed out in , the uncertainty of both TROPOMI and OCO-2 SIF is expected to lead to a certain spread between the data sets. In addition, we do not account for differences in acquisition times and viewing–illumination geometry, which can lead to additional uncertainties in this comparison. For reference, when comparing single footprints of TROPOMI SIF to aggregated OCO-2 SIF for June 2018 globally, found a r2 of 0.67; only additional aggregation leads to a r2 of 0.88. The mean deviation of TROPOMI SIF to OCO-2 SIF is close to the average standard deviation of TROPOMI SIF (0.4 mW m−2 sr−1 nm−1). In our analysis, from the 16 d product from TROPOMI SIF for April 2018 until March 2021 at 0.05∘, we observe an average error in the TROPOMI SIF of 0.43 mW m−2 sr−1 nm−1 for the CONUS. That error is close to the RMSE between instantaneous TROPOMI SIF and instantaneous OCO-2 SIF (0.37 mW m−2 sr−1 nm−1). To compare TROPOMI and OCO-2/3 SIF we aggregate the OCO-2/3 footprints to the same grid as our TROPOMI data (0.05∘). As we aggregate multiple OCO-2 or OCO-3 footprints to match one TROPOMI grid cell at 0.05∘, the certainty of the OCO measurements increases, and therefore the RMSE between TROPOMI and OCO SIF decreases.

Figure  presents a detailed comparison of SIFnet and the downscaled SIF in four US cities. The first column shows the SIFnet estimate, the second the downscaled SIF from , the third the difference between SIFnet and downscaled SIF, and the last column the difference in correlation of the high-resolution SIF estimates to combined OCO-2 and OCO-3 data on a 0.02∘ grid multiplied by the L1 norm between the SIFnet and the downscaled SIF. The column on the right highlights both regions where the differences in predicted SIF are large and which product is performing better. As such, the right column will show white in areas where the difference in predicted SIF is small or the correlation with OCO is similar. While we observe large differences in predicted SIF for the urban areas (column 3), we do not find one product to perform systematically better in urban areas. This likely indicates the complexity in the SIF signal arising from urban areas. Additionally, urban areas make up a small fraction of the overall land mass and, as such, do not represent a large share of the training data in SIFnet. These factors likely contribute to the heterogeneous performance observed in the right column of Fig. .

However, there are some notable successes of SIFnet in urban areas that can be mapped directly to features in the urban area. Figure  shows both SIFnet and the downscaled SIF along with NIRv from MODIS and a true color image of Chicago. A feature clearly stands out in both the downscaled SIF and NIRv image. This is a region with missing NIRv and effectively no downscaled SIF. However, SIFnet does not show a strong gradient here. This region corresponds to the Chicago airport. In the MODIS NIRv image it is visible that there are no valid data available for that region for the 3 investigated years. The downscaling method from relies only on NIRv in the weighting function. If there are no data available for the region, they are interpolated in space and time. Here, it shows that the method seems to fail in urban regions where no MODIS NIRv signal is available. SIFnet handles this region better and seems to rely on other auxiliary data if there is no MODIS NIRv available. In Fig.  it is also visible that the SIFnet estimate correlates better with the OCO-X data than the downscaled SIF for the region of the Chicago airport.

Here, we develop a convolutional neural network (CNN) model named SIFnet to increase the resolution of TROPOMI SIF by a factor of 10. The novelty of our method consists of using coarse-resolution SIF measurements together with high-resolution auxiliary data as model input to estimate high-resolution SIF. After optimization and hyperparameter tuning of SIFnet, the estimated SIF at 500 m resolution yields an r2 and RMSE of 0.92 and 0.17, respectively, when compared against validation data (Fig. ). We further compare the output of SIFnet against a recently developed downscaling method to estimate high-resolution SIF  and evaluate both methods against independent observations from the Orbiting Carbon Observatory 2 and 3 (OCO-2/3). SIFnet is found to perform systematically better than the downscaling approach when compared against independent measurements. Through interpretable machine learning methods, we identify the key features that SIFnet utilizes to accurately predict high-resolution spatial patterns of SIF. We find that SIFnet relies heavily on the low-resolution SIF feature (SIFLR) and the vegetation index NIRv (Fig. ).

SIFnet is a multi-layer CNN that increases the spatial resolution of the TROPOMI SIF by a factor of 10. Our model uses auxiliary data sets related to gross primary productivity and SIF as inputs and yields a high-resolution SIF estimate. The model is trained using three years of data from Asia, Europe, Africa, and South America. North America is used as the validation data set. Our loss function is comprised of two terms: the mean squared error and the structural dissimilarity index. The combination of these two terms improved the performance of our model.

SIFnet was further compared to the recent downscaled SIF product developed by . The two high-resolution estimates showed pronounced differences across the western US drylands. This difference is particularly interesting because these drylands tend to be low-productivity regions and traditionally have been difficult for SIF to accurately capture due to the low signal-to-noise ratio. Both high-resolution SIF estimates were compared to independent observations from OCO-2/3. SIFnet performed systematically better than the downscaled SIF (r=0.78 for SIFnet, r=0.72 for downscaling). SIFnet and the downscaling method also yielded differences in urban regions. However, there was substantial heterogeneity in the performance of SIFnet and downscaling in urban areas. One product did not perform systematically better than the other within urban areas. The mixed results in urban areas likely relates to both the complexity of the photosynthetic activity in urban areas, as well as the lack of training data, as urban areas represent a small fraction of the total landmass.

We adapted techniques from the area of interpretable machine learning to assess the key features driving SIFnet. Specifically, we conducted random permutations to input data sets and assessed the impact on the resulting RMSE. From this, we found that SIFnet relies most heavily on the low-resolution SIF feature (SIFLR). The second most important factor is the MODIS vegetation index NIRv. NIRv is also found to outperform the recently proposed kNDVI vegetation index, in contrast to . The interpretable machine learning approach also allowed us to identify spatial regions of importance for the different parameters. Interestingly, SIFnet relies more heavily on NIRv in the western drylands where the SIF signal-to-noise ratio is low. This implies that SIFnet is picking up on key physics that lead to the improved performance relative to the downscaling method. Overall, SIFnet represents a robust method to infer continuous high-spatial-resolution information about processes related to gross primary productivity.