dataesr/scientific-paragraphs-categorization huggingface dataset:
Landsat Surface Reflectance Dataset We used a 2002-2017 time-series of Landsat images (5 TM, 7 ETM+, and 8 OLI) in the GEE online platform to acquire the pH of the Nhecolândia lakes (Figure  2 ). To ensure the use of the real pixel values, we used calibrated top-of-atmosphere (TOA) reflectance images that are included in the "USGS Landsat (5 TM, 7, and 8) Collection 1 Tier 1 TOA Reflectance" on GEE. These collections are atmospherically corrected by the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS)  [49]  that includes cloud, shadow, water, and snow masks produced using per-pixel saturation analysis and a CFMASK filter  [50]. The orthorectified and atmospherically corrected images have Level-1 Terrain Precision (L1TP) processed data, with well-characterized radiometry and inter-calibration across the different Landsat sensors  [50].
Field Sample Data Ground-truth data comprised a subset of 130 georeferenced surface water samples collected at a depth of approximately 20 to 30 cm (see location on Figure  1C ) used for validation of the model. The pH values were acquired using a pH-meter (Hanna Instruments HI98140), with stabilization between 10 s and 1 min. Each sample contains pH information collected at the time of data acquisition measured directly on the surveyed lake. Some lakes were sampled more than once but in different years or seasons. The measured pH values within the 130 water samples followed a bimodal distribution (see Figure  S1  in the Supplementary Materials), with values centered between 5. 7 and 8. 2. We also observed the occurrence of hypersaline lakes with pH values ranging from 9 to 10. The selected samples were well-distributed within the Nhecolândia region and were collected by different groups in different field campaigns.
Thus, the field sample data represent the average pH condition of the Nhecolândia lakes. All lake categories (freshwater, saline, and oligosaline) were surveyed. The final database presented samples collected by different groups, as follows: (i) 65 samples collected by the authors; 4 in 2009, 39 in 2014, 12 in 2015, and 10 in 2016; and (ii) 66 water samples measured by Costa et al. [12]  in 2008. The water pH values of Costa et al. [12]  were also determined in situ by using a Hydrolab Quanta G multimeter. Samples not related to water bodies, as well as those located in lakes smaller than the minimum possible area mapped by the Landsat data (900 m 2 ), or in areas sampled in swampy regions adjacent to the lakes (vazantes) were excluded to avoid outliers. The resulting databases were combined and subsequently divided into independent validation and training datasets to predict the regional occurrence of saltwater and freshwater lakes according to the methodology described in the following sections.
The objective was to select the best global linear model (full collection from 2002 to 2017 or collections with specific seasonal filters) to predict the dependent variables. Unlike linear methods, the symbolic regression searches both the parameters and the form of the equations, allowing the automatic generation of GP functions  [60]. The resulting equations of the regression between in situ pH and spectral bands (Landsat bands 1, 2, 3, 4, 5, 7, and synthetic bands NDVI, AWEInsh, NDWI, and MNDWI), were used to simulate pH bands along the Nhecolandia lakes. We randomly classified the 130 samples into subsets, comprising training (90 samples) and independent validation datasets (40 samples). The training dataset was used to predict values of the pH and to derive the regression models to be used for generating the pH image band according to median Landsat time-series collection.
The independent validation dataset was used to evaluate the quality of the regression models. We used the coefficient of determination (R 2 ), root-mean-square error (RMSE), and Akaike's Information Criterion (AIC) to access the accuracy of the predicted pH values against the measured validation values. The RMSE and AIC are defined as RMSE = 1 n n i=1 (ρ i -ρi ) 2 , ( 5 ) AIC = Nln        1 n n i=1 (ρ i ρi ) 2        + 2P, (6) where ρi and ρ i are the observed and predicted pH values, respectively, i represents the lake sample, P is the number of parameters used, n is the total number of observations, and Nln is the natural logarithm. The best model is the one with RMSE values closer to 0 and smaller AIC values. The GP uses a set of arithmetic and complex operators to model the relationship between pH and auxiliary Landsat pixels, by testing different operators until the best prediction model is achieved.
Spatial Modeling of Nhecolândia Lakes By using GEE, we were able to analyze over 2081 scenes of median values of spectral signatures with cloud-free pixels for the 2002-2017 time-series, including the TM, ETM, OLI bands, and synthetic indexes. The use of five seasonal filters was necessary because the Pantanal is seasonally flooded. The correlation values between explanatory bands and dependent variables considering five different scenarios, as well as the results of SMLR analysis, are summarized in Table  1. The highest correlation was observed by considering the high-water season Landsat collection from 2002 to 2017, with an R 2 of 0. 72 and RMSE of 0. 90. It was followed by the collection for the same period, but with no seasonal filter. We also verified the correlation of pH values with spectral data at sampled locations for the same sample data acquisition period, considering separately the period of the two field campaigns (high water season in 2008 and the period from 2014 to 2017).
502868058; b: 0. 000268732; c: 5. 669416074e pH (SMLR) = a -b * AWEInsh + c * band 2 -d * band 4 + e * NDWI, (8) where pH (SMLR) is the pH predicted by SMLR, band 2, and 4, AWEInsh and NDWI are the Landsat bands from 2002 to 2017, and the letters represent coefficients of the equation, as follows: a: 1. 36338; b: 0. 00110; c: 0. 00818; d: 0. 00392; e: 0. 00120. Genetic programing based on symbolic regression (Equation (  7 )) resulted in a more complex solution than SMLR (Equation (  8 )), but the correlation results were better in explaining the spatial variability of pH in the Nhecolância lakes considering both the validation and total sample dataset (Figure  4 ). The model obtained by GP explained more than 85% of the spatial pH variability (Figure  4c ), while the model generated by SMLR explained 74% of the variability (Figure  4d ), taking into account the validation dataset.
Moreover, the correlation considering both validation and training datasets (Figure  4a, b ) is better in the GP regression model, with an R 2 of 0. 81 against 0. 73 for the SMRL model. The GP model proved the most accurate for modeling pH in the Nhecolândia using explanatory Landsat bands (Figure  4 ). The GP model based on the validation dataset had R 2 , RMSE, and AIC values of 0. 85, 0. 55, and -32. 8, respectively, while the values of the R 2 , RMSE, and AIC for SMLR were 0. 74, 0. 85, and -27. 06, respectively. As a result, Equation (7) was used to generate a synthetic Landsat band in the GEE cloud platform, referred to here as the pH Band. This proposed band allows for the systematic segmentation of saline and freshwater lakes. The Nhecolândia saline lakes tend to be more perennial, normally devoid of vegetation, and with high concentrations of cyanobacteria; on the other hand, the freshwater lakes are mostly temporarily affected by the flood pulse and have large amounts of aquatic macrophytes [7,  13, 29, 68, 69].
However, it is important to highlight that pH and electrical conductivity (EC) have a direct correlation in the Pantanal lakes. Furian et al. [7]  analyzed more than 300 samples collected on surface water and found a high correlation (R² = 0. 87) between pH and the logarithm of EC. Therefore, it is safe to assume that pH is a direct proxy of water salinity in the Nhecolândia lakes. In our dataset, we found an R² slightly smaller (0. 745, see graph in the Supplementary Material); however, one should remember that our dataset uses field samples collected in different campaigns and by distinct groups, once we also used samples from Costa et al. [12] , which could explain the smaller value of the R 2. We believe the model properly represents the reality observed in the field as we could capture the variation in pH values in the Nhecolândia region with a high correlation between field samples and our pH band.
Therefore, it is safe to assume that pH is a direct proxy of water salinity in the Nhecolândia lakes. In our dataset, we found an R 2 slightly smaller (0. 745, see graph in the Supplementary Materials); however, one should remember that our dataset uses field samples collected in different campaigns and by distinct groups, once we also used samples from Costa et al. [12] , which could explain the smaller value of the R 2. We believe the model properly represents the reality observed in the field as we could capture the variation in pH values in the Nhecolândia region with a high correlation between field samples and our pH band. However, it is important to highlight that we did not aim to propose a universal pH-predicting model for the region. Our goal was to find a way to predict pH in non-observed lakes, in order to create a systematic map of lakes' pH, which is essential to further understand the geochemistry of the region.
Acknowledgments: The authors are grateful to the  São Paulo Research Foundation (FAPESP)  for financial support of this project and  Ec2co-INSU (National Institute of Sciences of the Universe ). We thank the  National Council for Scientific and Technological Development (CNPq)  for the postdoctoral scholarship to OJRS and  FAPESP  for the postdoctoral scholarship to ERM. We are thankful for the editors and reviewer's comments and support.
Methodological limitations and potential ways forward Machine learning methods can learn arbitrarily complex functions and provide a nearly perfect model of a phenomenon if they are fed with the right data and trained thoroughly. Thus the quality, quantity and completeness of the input data determine the quality of the output. In the following, we discuss the relevance of limitations associated with data from the FLUXNET network and of the limited capabilities of representing all relevant factors by observable predictor variables. We also outline potential strategies for improvements, both overall and with respect to machine learning approaches specifically. The continued and rapid devel-opment of machine learning notwithstanding, we believe that the FLUXCOM approach is at present more limited by available "information" rather than by available machine learning methods.
Differences due to instrumentation and maintenance pose another potential source of uncertainty. Additionally, the energy balance closure gap at FLUXNET sites is still not resolved  (Stoy et al. , 2013) , while it remains unclear to what extent this is relevant for CO 2 fluxes  (Leuning et al. , 2012). Systematic errors in GPP and TER derived from the flux partitioning method of NEE based on night-time data  (Reichstein et al. , 2005)  may arise due to the neglected effect of inhibited photorespiration during daytime  (Keenan et al. , 2019; Wehr et al. , 2016). Nevertheless, all these issues together seem to be relatively small compared to the predominant patterns of variability in EC data, e. g. seasonal variations, that are very consistent across FLUXCOM and independent observation-based data streams shown here.
The relatively strict quality controls on the flux training data  (Tramontana et al. , 2016)  may have been instrumental here. The trade-off between data quality and training data volume was not explicitly studied in FLUXCOM, and related experimental setups would be desirable to gauge the robustness of the global products shown here. Even small systematic errors in EC data could degrade important signals such as interannual variability, trends, annual sums of NEE or subtle differences between sites related to functional properties (e. g. radiation use efficiency). Systematic errors that would be prevalent across the network would result in systematic biases of derived global fluxes. For global GPP and energy fluxes  (Jung et al. , 2019) , the values obtained from FLUX-COM are generally consistent with current knowledge but our ability to independently quantify such fluxes is also limited.
, 2015). However, what matters eventually for machine learning methods is how well the predictor space, rather than geographic space, is sampled. To assess this, we developed an extrapolation index (EI) that estimates the expected additional relative error of a flux prediction due to a large distance to the nearest training datum in the predictor space (S2). We applied this method for GPP and FLUXCOM-RS training data as an example, and we found that the conditions that are least well represented by FLUXNET are associated with primarily ex-tremely cold and dry regions (Fig. 10 ). Surprisingly, the humid tropics are well represented in the predictor space, suggesting that the environmental conditions represented by the predictor set are well sampled by the data from FLUXNET sites. The extremely cold and dry conditions that seem to constitute the biggest extrapolation issues are typically associated with small GPP fluxes and thus also small prediction errors.
To account for that, we spatialized the expected GPP error of the RS ensemble (Figs. 10, S2 for details), which largely scales with GPP magnitude but also shows patterns of larger expected errors in semi-arid regions than those expected from flux magnitude alone. The multiplication of the expected GPP error with the extrapolation index provides the extrapolation severity index (ESI) that shows where poor FLUXNET sampling likely increases the absolute prediction error strongly. According to these results, subtropical semiarid regions, in particular India, appear most affected, suggesting that GPP upscaling from FLUXNET would benefit most strongly from improved data availability for towers representing these conditions. Despite these limitations of data, we found excellent consistency of FLUXCOM GPP seasonal cycles with SIF over these regions, which was in fact much better than the consistency between TRENDY models and SIF.
Management We are presumably lacking important information on anthropogenic management effects, in particular for crops  (Guanter et al. , 2014)  but also for forests. This is primarily due to a lack of information on, e. g. crop type, fertilizer application, irrigation, harvest or thinning at FLUXNET sites, but also due to the still-limited number of crop sites to provide sufficient information on relevant predictors therein. Accounting for the management effects in the FLUXCOM approach either by explicit management information or implicitly by adequate remote sensing data may also help improve the predictions of IAV of local-scale carbon fluxes, in particular with cross-validation since most FLUXNET sites are subject to some degree of management.
Water stress Site-level cross-validation analysis  (Bodesheim et al. , 2018; Tramontana et al. , 2016)  indicated that soil moisture effects on carbon fluxes are not always well captured. In RS+METEO, moisture effects are explicitly addressed by a simple meteorology-driven water availability index. The RS setup relies entirely on indirect information encoded in remotely sensed surface properties such as vegetation indices and land surface temperatures. The comparison of FLUX-COM GPP seasonal cycles with SIF yielded excellent agreement, also in water limited systems, and studies on drought effects using the GPP RS product  (Flach et al. , 2018; Orth et al. , 2019; Walther et al. , 2019)  found plausible patterns that were consistent with independent data on large scales. Nevertheless, we should strive further to improve water stress effects in the upscaling approach given its significance.
Product properties The success of incorporating novel informative data of site properties in the FLUXCOM approach is always contingent on the quality of the corresponding global gridded products. Systematic differences between a predictor variable used for training at the site level and global forcing data, as well as any potential artefacts due to retrieval issues or merging different data records spatially or temporally, propagate to global flux products. Future improvements of the FLUX-COM approach will thus require progress in other research fields with emphasis on the processing, correction and harmonization of Earth observation products. Especially for remotely sensed data, strategies to bridge scales of satellite pixels, overpass times and repeat cycles to continuous measurements of flux footprints are needed.
Exploiting temporal data structures The machine learning methods employed in FLUXCOM are classic ones, while novel approaches could bring further improvements. One conceptual limitation of all machine learning methods used in FLUXCOM is that they assume independent and identically distributed (variables, and thus they do not respect or exploit temporal structures in the training data. This problem can be remedied by using other machine learning methods based on convolutions. For example, recurrent neural networks (RNNs) were designed for time series and can account for dynamics such as ecosystem lag and memory effects on carbon flux variability. Conceptually, lag and memory effects emerge due to the effect of unobserved ecosystem state variables. RNNs can potentially counteract the lack of a relevant state variable in the predictor set if the state variable's instantaneous effect is encoded in the temporal history of other predictor variables (e.
The principle of transfer learning is to learn relevant features from a more densely observed proxy variable of the actual target and use the feature representation for learning the target  (Pan and Yang, 2010). The learning of the proxy variable can be done either prior to or simultaneously with the actual target such that information from a much larger sample of the proxy can be transferred to the sparsely observed target variable. This approach could be applicable to the upscaling of FLUXNET GPP by using remotely sensed SIF as a proxy and thereby alleviate issues related to small sample size (e. g. extrapolation) but also aid the extraction of small but relevant signals (e. g. IAV). Spatial structures in high-resolution SIF data may further encode effects of management or topographically controlled soil moisture variations that could be exploited with CNNs and improve predictions.
Hybrid approaches, i. e. the integration of a machine learning method with process understanding and physical constraints, are another promising avenue. This allows for different strategies and levels of complexity  (Reichstein et al. , 2019)  and could also greatly help in regularizing machine learning predictions to be sensible under extrapolation conditions. In the context of FLUXCOM, for, say, constraining the anticipated weak signal of CO 2 fertilization in observations within theoretically derived bounds, hybrid modelling would allow this relevant yet observationally poorly constrained dynamic to be incorporated. If the hybrid approach features the conceptualization of fluxes and pools as in process models, it would also allow for constraints by multiple complementary data streams simultaneously.
Conclusions The FLUXCOM initiative generated a large ensemble of global carbon flux products for two defined setups that differ in the set of predictor variables and spatial-temporal resolution. The ensemble is comprised of 120 products using up to nine machine learning algorithms, two flux-partitioning variants for GPP and TER, and five meteorological forcing data sets. The large and systematically generated ensemble allows for assessing and studying uncertainties of the fluxes as well as the approaches used in FLUXCOM. We assessed FLUXCOM GPP and NEE patterns against remotely sensed sun-induced fluorescence (SIF), atmospheric inversions and process model simulations from the TRENDY initiative. We found strong consistency of FLUXCOM with SIF and atmospheric inversions with respect to seasonal variations, highlighting FLUXCOM's suitability to evaluate and constrain seasonal cycles for processed-based and top-down approaches.
Moving forward, increasing the size of the FLUXNET network and improving its quality, standardization and coverage will both improve quality and reduce uncertainties in the upscaling approach. This holds especially true with respect to signals that are important but relatively small and difficult to extract such as interannual variability or trends. Increasing the number of tropical sites alone would also help constrain global flux magnitudes, and, in particular, would help resolve the large tropical carbon sink shown by FLUXCOM but missing in atmospheric inversions. Based on the number of registered FLUXNET sites alone, an approximate 5-fold www. biogeosciences. net/17/1343/2020/ Biogeosciences, 17, 1343-1365, 2020 increase in the number of sites with available data seems feasible in theory, if all respective researchers would contribute their flux data to the global community effort.
This indicates that any efforts to improve eddy covariance data, sharing, harmonization and processing are crucial. Beyond extending the data frame, the current FLUXCOM intercomparison suggests that the next phase of methodological developments should be to move away from predetermined setups and instead towards a set of dedicated experiments that explore novel strategies of data integration with machine learning methods (e. g. deep, transfer and hybrid approaches) and, more importantly, the diversity in the potential predictor space from Earth observation data. Within FLUXCOM, we find the largest differences between RS and RS+METEO setups which primarily differ in the set of input predictor variables. Thus, the current approach of upscaling FLUXNET measurements seems more information rather than algorithm limited.
Overall, the success of FLUXCOM approach depends on the interplay of many different factors. Monitoring our progress will be essential but challenging and must combine site-level cross-validation, cross-consistency checks with global independent data streams, novel and dedicated experiments, and tailored validations of methods with artificial data similar to observation system simulation experiments. Despite the many challenges, integrating eddy covariance ecosystem-scale fluxes, Earth observation data and machine learning methods has already proven valuable in many respects despite being a comparatively new field. An exciting and challenging future lies ahead: the contribution of experts in different fields combined with open and real-time data sharing could lead to a unique semi-operational carbon monitoring system.
This in turn provides a promising perspective to unify and synergistically exploit data-driven biospheric bottom-up and atmospheric top-down approaches. Data availability. Monthly carbon flux data of all ensemble members as well as the ensemble estimates from the FLUXCOM initiative (http://www. fluxcom. org, last access: 27 February 2020) are freely available (CC4. 0 BY licence) from the data portal of the Max Planck Institute for Biogeochemistry (https://www. bgc-jena. mpg. de/geodb/projects/Home. php, last access: 27 February 2020, Boenisch, 2020) after registration. Choose "FluxCom" in the dropdown menu of the database and select FileID 260. The users will be provided with access to an ftp server. The ftp directory is structured in a consistent way and stores files with the consistent naming convention in NetCDF-4 format (see Sect.