dataesr/scientific-paragraphs-categorization huggingface dataset:
The correct behavior of a program is modeled as a set of logical constraints, thus restricting the possible traces to consider. Traces are ordered permutations containing both control flow operations and memory accesses and are constrained by axioms tailored to data race and sequential consistency. The theory supports any logical constraints to determine correctness, it is then possible to encode a specification on multithreaded programs as such. However, allowing for arbitrary specifications to be encoded while supported in the model, is not supported in the provided tool (RVPredict). In  [27] , the authors present ExceptioNULL that target nullpointer exceptions. Violations and causality are represented as constraints over actions, and the feasibility of violations is explored via an SMT constraint solver.
GPredict  [36]  extends the specification formalism past data races to target generic concurrency properties. GPredict presents a generic approach to reason about behavioral properties and hence constitutes a monitoring solution when concurrency is present. Notably, GPredict requires specifying thread identifiers explicitly in the specification. This makes specifications with multiple threads to become extremely verbose; unable to handle a dynamic number of threads. For example, in the case of readers-writers, adding extra readers or writers requires rewriting the specification and combining events to specify each new thread. The approach behind GPredict can also be extended to become more expressive, e. g. to support counting events to account for fairness in a concurrent setting. Furthermore, GPredict relies on recording trace of a program before performing an offline analysis to determine concurrency errors  [36].
We evaluated the results against regional reference maps of surface water based on MODIS, JERS-1, and PALSAR, and against the Global Inundation Extent from Multi-Satellite (GIEMS) dataset. CYGNSS water fractions represent well the spatial distribution and the seasonality of inundation extent for most of the tropical wetlands, with a global RMSD of 15. 0 % against GIEMS. We found a likely overestimation when the soil moisture (SM) exceeds 0. 3-0. 4 cm 3 ∕cm 3 , i. e. for saturated soils where GNSS-R reflections are coherent, as the SM was not parameterized in our retrieval model. Despite this, the results are consistent in the entire intertropical band including over densely vegetated areas. These weekly water fractions for 2018-2019 with a 0. 1 • pixel size are distributed to contribute to further comparisons and hydrological researches.
L-band SAR images were used to map inundations in the densely forested Amazon and Congo basins  (Hess et al. , 2003 (Hess et al. , , 2015;; Betbeder et al. , 2014; Chapman et al. , 2015; Rosenqvist et al. , 2020). However, the use of SAR images was limited by a low temporal resolution before the launch of Sentinel-1, the low availability of L-band data, and the complexity to identify water signatures at large spatial scales with varying geophysical properties, incidence angles and different polarizations. This caused SAR data to be mainly used for near-real time flood monitoring at small spatial scales  (Pulvirenti et al. , 2011; Pierdicca et al. , 2013; Westerhoff et al. , 2013; Martinis et al. , 2015; Twele et al. , 2016) , but not to derive a global surface water extent dataset. Recently,  Martinis et al.
All these studies were performed over small regions, as the detection of surface water at global scale is hampered by changes in the geophysical properties of the reflecting surface. Recently,  Zeiger et al. (2022)  demonstrated that CYGNSS reflectivity could be used to derive a surface water extent dataset in the entire intertropical area despite a large attenuation of the GNSS-R signals in dense forests. A clustering method based on Dynamic Time Warping (DTW) was applied to the time-series of reflectivity, and five clusters were defined depending on their inundation dynamics. This study dealt with the detection of surface water but did not compute the fractional inundation (or water fraction) in each 0. 1 • pixel. Chew et al. (2023)  provided the first fractional inundation estimations based on CYGNSS, with a physical rather than statistical approach.
, 2021 (Li et al. , , 2022;; Collett et al. , 2022; Russo et al. , 2022; Chapman et al. , 2022). This characteristics permitted to detect water bodies with a width of ∼100 m, including floods in densely vegetated areas, i. e. with an AGB of 250-300 Mg/ha  (Li et al. , 2021). However, although being very promising for surface water monitoring and for phase-delay altimetry  (Cardellach et al. , 2004; Li et al. , 2017) , this dataset is limited by the low amount of raw IF tracks recorded. Because the complex information is very large, it is incoherently averaged over 0. 5 s (1 s before July, 2019) to produce the Level 1 Delay Doppler Maps (DDM) that represent the main CYGNSS data record. An operational flood monitoring based on raw IF data is not possible, at least before the launch of the future ESA's HydroGNSS mission planned for 2024  (Unwin et al.
(2022)  and time-series of CYGNSS reflectivity are used to further derive weekly, 0. 1 • (∼11 km at the equator) water fractions in the full CYGNSS coverage. Here, we build a statistical model using reference water fraction maps in an attempt to exploit the potential of CYGNSS for the monitoring of surface water extent spatiotemporal variations at both global and regional spatial scales. We particularly focus on producing realistic estimations over vegetated wetlands which cover a large fraction of the intertropical band and are poorly monitored up to now. The datasets used in this study are presented in Section 2, and Section 3 presents the water fraction retrieval methodology. The results and discussions of this work are given in Sections 4 and 5, respectively.
For the purpose of this study, we used the daily files from CYGNSS Level 1 Science Data Records, v3. 0 (CYGNSS, 2020) distributed by the Physical Oceanography Distributed Active Archive Center (PODAAC). We used a 1-year dataset from August 2018 to July 2019 over the entire CYGNSS coverage. Only one year of data was used as in  Zeiger et al. (2022)  due to the high computational load required and for testing first the methodology. A further application to the ongoing 7-year CYGNSS records is likely. The study period corresponds to a 1 Hz sampling of the measurements (except for the last month), while twice more observations are performed monthly in the following years.
GIEMS surface water extent We need external inundation datasets to compare with CYGNSS Water Fraction estimations. There are two global, long-term and lowresolution (0. 25 • ) surface water extent products based on a combination of passive and active microwave observations: the Global Inundation Extent from Multi-Satellite v2 (GIEMS-2,  Prigent et al. , 2020)  and the Surface Water Microwave Product Series  (SWAMPS, Schroeder et al. , 2015). We used GIEMS as numerous issues were pointed out in SWAMPS in an  inter-comparison (Pham-Duc et al. , 2017) , although some were further corrected in the later releases  (Jensen and Mcdonald, 2019). GIEMS surface water extent maps for 2018 and 2019 have been specifically processed for this study, averaged over 10 days. CYGNSS and GIEMS-2 water fractions are independent (see Section 3) and feed an inter-comparison of inundation extent and dynamics in both datasets (Section 4).
Regional maps of the flood dynamics GIEMS spatial resolution is 0. 25 • (∼28 km at the equator) which limits the detection of small water bodies. The sensitivity of CYGNSS to small river streams is therefore much higher  (Zeiger et al. , 2022). In this study, high resolution surface water extent maps were employed both in the retrieval methodology (see Section 3) and to compare with CYGNSS output water fractions. Global, high resolution datasets based on optical sensors like Landsat  (Pekel et al. , 2016 ) cannot detect flooded forests and so they are not suitable for comparison with CYGNSS in the intertropical band. We finally used a set of regional maps based on both optical and SAR data, which represent the diversity of the pan-tropical climate regions. The first dataset consists in flood maps at 500 m spatial resolution based on MODIS surface reflectances from the MOD09A1 product (level 3/v6, 8-day binned).
They are retrieved with the multi-threshold approach from  Frappart et al. (2018)  and  Normandin et al. (2018)  as a simplification of  Sakamoto et al. (2007) , using the Enhanced Vegetation Index (EVI,  Huete et al. , 1997)  and the land surface water index (LSWI,  Xiao et al. , 2005)  values. We computed MODIS flood maps for the 2018-2019 study period over three distinct climate regions: the Parana/La Plata river basin presenting flooded savannas and higher vegetation in the north, the Lower Mekong basin (LMB) with paddy fields and a strong seasonal flood signal around the Tonle Sap, and the Inner Niger Delta (IND) with low vegetation and bare soils. Because MODIS is unable to detect flooded forests, especially in the Amazon and Congo basins, we need other high-resolution reference datasets covering dense tropical forests.
This kind of dataset is rare and mostly based on L-band SAR images, which penetrate better the canopy layers. We used here the dual-season wetland classification in the Amazon basin from  Hess et al. (1995 Hess et al. ( , 2003)). Based on JERS-1 Lband mosaics for the reference year 1995-1996, it provides a 100 m wetland mask at both low and high water stages  (October-November 1995 and May-June 1996, respectively). Other similar datasets exist (see  Fleischmann et al. , 2022  for a comparison) but this one is still considered as a reference and has been widely used to compare with later studies. Finally, flood maps at high and low water stages in the Cuvette Centrale of Congo were extracted from  Betbeder et al. (2014). They are based on six images from the Phased-Array type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) in 2009 and 2010, combined with Enhanced Vegetation Index (EVI) from MODIS and canopy height estimated with Light Detection and Ranging (LIDAR) data.
Other datasets The AGB represents a static information and is related to the total woody volume, but the optical signals from MODIS can be affected by the vegetation growth. To evaluate the influence of the vegetation on the statistical comparisons performed, we used the Normalized Difference Vegetation Index (NDVI) maps from the MOD09A1 product (8-day binned level 3, version 6), derived from MODIS surface reflectance measurements. Moreover, our retrieval model neglects the influence of moisture variations to only consider the attenuation of GNSS-R signals by the vegetation (see Section 3). We nevertheless evaluated how the comparisons performed between CYGNSS, GIEMS, and MODIS Water Fractions vary according to the SM content. For this, we used the SMAP enhanced L3 9 km SM version 5 distributed by the National Snow and Ice Data Center (NSIDC)  (O'Neill et al.
, 2021). The 9 km spatial resolution of SMAP enhanced L3 SM is close to the 0. 1 • CYGNSS maps produced (see Section 3), and a global coverage on Earth is obtained every 3 days. All files from August 2018 to July 2019 were downloaded and further regridded to obtain reference SM values. Additionally, we filtered out deserts and open water areas as these targets were shown to produce false alarms and missed surface water detections, respectively  (Al-Khaldi et al. , 2021a; Zeiger et al. , 2022). For this, we used the static open water and flood masks from the Regularly Flooded Wetland (RFW) dataset  (Tootchi et al. , 2019). Finally, the European Space Agency's (ESA) Climate Change Initiative (CCI) land cover maps were used for the input land cover information required (ESA, 2017).
Resampling of the datasets The ancillary datasets were resampled to correspond to the spatiotemporal resolution of CYGNSS estimated water fractions (0. 1 • and 7 days). The average and standard deviation of AGB and NDVI per pixel were computed. We also upscaled the 500 m MODIS, 100 m JERS-1 and 100 m PALSAR flood maps at 0. 1 •. We computed the reference water fractions as a ratio between the number of flooded sub-pixels and the total number of sub-pixels in a given 0. 1 • pixel. Finally, we resampled SMAP SM maps from 9 km to 0. 1 • using a simple linear interpolation, and we computed the weekly averaged SM for all grid pixels. The version of GIEMS we used is provided at ∼25 km spatial resolution and 10-day temporal resolution. We performed the statistical comparison between CYGNSS and GIEMS at 7 days and 0.
1903 m), 𝑅 𝑡 and 𝑅 𝑟 are the ranges (distances from the specular point to the transmitter and the receiver, respectively), 𝜃 𝑖 is the incidence angle, 𝐺 𝑟 is the receiver antenna gain, and 𝑃 𝑡 𝐺 𝑡 is the GPS Equivalent Isotropically Radiated Power (EIRP). In this study, the reflectivity is expressed in linear units and not in decibels. While the formulation in decibels was adopted in most of the literature (e. g. Chew and Small, 2020b; Chew et al. , 2023) , a linear scale is more convenient to formulate the linear relationships further used to invert water fractions (see Section 3. 2). CYGNSS reflectivity was further normalized at a 0 • incidence (nadir measurement) to correct the variations of the received power with the varying incidence angle, following  Zeiger et al. (2022) : 𝛤 = 𝛤 (𝜃 𝑖 = 0 • ) = ( 4𝜋 𝜆 )2 𝑃 𝐷𝐷𝑀 (𝑅 𝑟 + 𝑅 𝑡 ) 2 𝐺 𝑟 𝐺 𝑡 𝑃 𝑡 × 1 𝑐𝑜𝑠(𝜃 𝑖 ) (2) We further aggregated the 1-year reflectivity dataset into a 7day, 0.
1 • spatiotemporal grid. The resulting time series of reflectivity showed gaps because CYGNSS measurements are not evenly distributed over the Earth's surface. To reduce the number of data gaps, we applied to each pixel a 30-day moving window weighted with a Gaussian distribution (𝜎 = 7 days). Then, we computed several statistical parameters describing the distribution of CYGNSS reflectivity in each pixel and per time step: the weighted average and standard deviation (𝛤 𝑚𝑒𝑎𝑛 and 𝛤 𝑠𝑡𝑑 , respectively), the median (𝛤 𝑚𝑒𝑑𝑖𝑎𝑛 ), and the nineteenth percentile (𝛤 90% ). These parameters allowed the detection of flooded pixels in  Zeiger et al. (2022)  owing to a clustering method based on Dynamic Time Warping  (Berndt and Clifford, 1994). In this study, they were further used to quantify the fraction of water contained in each pixel.
Inversion of the Water Fractions Fig. 1  shows the complete flowchart for the retrieval of CYGNSS water fractions (WF). After the preprocessing and the gridding steps previously described, a linear model is established between the WF to be retrieved and 𝛤 𝑚𝑒𝑎𝑛. Only the methodology is described in this section whereas the results are presented in Section 4. 1. We used the clustering results from  Zeiger et al. (2022)  to select pixels likely to be affected by floods. We also filtered out desert pixels as defined by a flood occurrence of zero and a land cover composed of at least 90% of bare soils, using the RFW dataset from Tootchi   (Chew and Small, 2020b; Loria et al. , 2020) , e. g. over the Lake Victoria and Tonle Sap  (Zeiger et al. , 2022). The reference WFs used to train our linear model were extracted from the regional surface water extent products based on MODIS over the Parana, Mekong, and Niger basins  (Normandin et al.
, 2018) , and based on JERS-1 over the Amazon  (Hess et al. , 2003 (Hess et al. , , 2015)). The latter only provides wetland maps at high and low water stages in 1995-1996. Similar dual-season maps were therefore extracted from the 8-day MODIS product in order to ensure the consistency of the learning dataset (see Fig. 1 ). The relationship between the reference WF and 𝛤 𝑚𝑒𝑎𝑛 is dependant on the biomass content of the pixel, as shown in Section 4. 1. The average GlobBiomass AGB of the pixel was therefore associated to each training and validation samples. Fig. 2b  presents the distribution of the AGB for samples were the reference WF dataset is either derived from MODIS (in blue, 19 228 samples) or JERS-1 (in green, 11 878 samples, Amazon). The latter perfectly complements the lack of MODIS data for pixels with moderate to high biomass, i.
e. for an AGB greater than 100-150 Mg/ha. Hence, the MODIS dataset contains 91. 1% of the samples with an AGB lower than 50 Mg/ha, and only 0. 6% of the samples with an AGB greater than 150 Mg/ha. The combination of both data sources makes the reference dataset consistent for an AGB range of 0 to ∼280 Mg/ha. Fig. 2c-d  present similar histograms for the reference WF and 𝛤 𝑚𝑒𝑎𝑛 samples, with a predominance of low WF and low 𝛤 𝑚𝑒𝑎𝑛 values. The linear model was established as follows. First, we defined randomly a 70% training and a 30% validation datasets from the reference WF, the average AGB, and 𝛤 𝑚𝑒𝑎𝑛 samples (see Fig. 1 ). We binned the training dataset along both the AGB (5 Mg/ha intervals) and the WF (2% WF intervals) dimensions and we computed the average 𝛤 𝑚𝑒𝑎𝑛 in each bin. This permits to compute pairs of (WF, 𝛤 𝑚𝑒𝑎𝑛 ) equally sampled.
We thus computed 𝑎 𝐴𝐺𝐵 and 𝑏 𝐴𝐺𝐵 as: 𝑎 𝐴𝐺𝐵 = 𝑎 3 × 𝐴𝐺𝐵 3 + 𝑎 2 × 𝐴𝐺𝐵 2 + 𝑎 1 × 𝐴𝐺𝐵 + 𝑎 0 (4) 𝑏 𝐴𝐺𝐵 = 𝑏 3 × 𝐴𝐺𝐵 3 + 𝑏 2 × 𝐴𝐺𝐵 2 + 𝑏 1 × 𝐴𝐺𝐵 + 𝑏 0 (5) where 𝑎 𝑖 and 𝑏 𝑖 , 𝑖 ∈ [0, 3] are the third order polynomial coefficients corresponding to the regression of 𝑎 𝐴𝐺𝐵 and 𝑏 𝐴𝐺𝐵 against the AGB, respectively. We computed the final robust coefficients as the average output of 100 random selections of the training dataset. The final equation for estimating 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 is: Coefficients of the third order polynomial fits of the slope and intercept against the AGB. 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 = 𝐴𝐺𝐵 3 ( 𝑎 3 𝛤 𝑚𝑒𝑎𝑛 + 𝑏 3 ) + 𝐴𝐺𝐵 2 ( 𝑎 2 𝛤 𝑚𝑒𝑎𝑛 + 𝑏 2 ) +𝐴𝐺𝐵 ( 𝑎 1 𝛤 𝑚𝑒𝑎𝑛 + 𝑏 1 ) + 𝑎 0 𝛤 𝑚𝑒𝑎𝑛 + 𝑏 0 (6) a 0 a 1 a 2 a 3 b 0 b 1 b 2 b 3 1. 67 -12. 1 𝑒 -3 6. 8𝑒 -5 0 -0. 30 5. 6𝑒 -3 -3. 5𝑒 -5 0. 6𝑒 -7.
Linear relation between 𝛤 𝑚𝑒𝑎𝑛 and the Water Fraction In every AGB interval of the learning dataset, the average 𝛤 𝑚𝑒𝑎𝑛 per 2% WF bin was computed. Fig. 3  presents some examples of the linear relationships found between these pairs of samples, for nine AGB intervals ranging from [0-5] Mg/ha to [265-270] Mg/ha. The Pearson's correlation between the 𝛤 𝑚𝑒𝑎𝑛 and WF samples is also shown for each subplot. As it can be seen, it is generally high when removing the influence of the vegetation. Also, it is important to note that both the slope (𝑎 𝐴𝐺𝐵 ) and the intercept (𝑏 𝐴𝐺𝐵 ) of the linear relationships vary considerably with the AGB. Indeed, Fig. 4  presents the slopes (red circles) and intercepts (blue triangles) extracted in all the AGB intervals. The red and blue curves are the third order polynomial fits of 𝑎 𝐴𝐺𝐵 and 𝑏 𝐴𝐺𝐵 against the AGB, respectively.
The results for both 𝑎 𝐴𝐺𝐵 and 𝑏 𝐴𝐺𝐵 are presented in Fig. 5. A second order polynomial sufficiently describes the relationship between the slope and the AGB. However, a third order term should be introduced to describe the relationship between the intercept and the AGB. The parameterization of these relationships in Eq. (  6 ) therefore employs third order polynomials. The ultimate coefficients 𝑎 𝑖 and 𝑏 𝑖 , 𝑖 ∈ [0, 3] are listed in Table  1  and correspond to the red and blue curves plotted in Fig. 4 , respectively. Note that 𝑎 3 = 0 in accordance with the results shown in Fig. 5a. Finally, bootstrapping was used to evaluate the robustness of the methodology across 100 iterations (results do not change significantly when increasing the number of iterations), with a random selection of the 70% training and 30% validation datasets.
The coefficients 𝑎 𝑖 and 𝑏 𝑖 , 𝑖 ∈ [0, 3] were determined at each iteration using the training dataset, and were used to compute predicted WF with the validation dataset. The RMSE and the Pearson's correlation were evaluated between the predicted and reference WF. Table  2  presents the results over 100 iterations for the entire validation dataset and for distinct 50 Mg/ha AGB intervals. The RMSE is around 0. 20 (20%) over the entire AGB range. The best RMSE observed for an AGB greater than 250 Mg/ha could be a consequence of the lower average water fraction found under dense vegetation covers. A larger deviation between minimum and maximum results and lower correlations are indeed observed for the 250-300 Mg. ha AGB interval.
Comparison of CYGNSS Water Fractions with GIEMS Our 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 estimations were first compared to the fully independent estimations from GIEMS-2 (hereafter 𝑊 𝐹 𝐺𝐼𝐸𝑀𝑆 ) over the entire coverage of CYGNSS (±38 • latitude). The resampling of GIEMS-2 to a 7-day temporal resolution and the upscaling of CYGNSS to a 0. 25 • spatial resolution were described in Section 2. 6. The global Root Mean Square Deviation (RMSD), bias and Pearson's correlation (R) were computed between 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 and 𝑊 𝐹 𝐺𝐼𝐸𝑀𝑆. First, only the samples with non-null WF in both datasets were considered. We found a bias of -8. 9% and a RMSD of 18. 3% which falls to 15. 9% when the bias is removed first (i. e. unbiased RMSD), with a correlation of 0. 53 between both datasets. The global bias corresponds to lower 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 than 𝑊 𝐹 𝐺𝐼𝐸𝑀𝑆 , which is consistent with further results and especially Fig.
11. The coverage of non-null WF is also more restrictive in 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 (∼8%) than in 𝑊 𝐹 𝐺𝐼𝐸𝑀𝑆 (∼16%). When selecting all estimations with a non-null 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 only for the computation of the statistics (no condition over 𝑊 𝐹 𝐺𝐼𝐸𝑀𝑆 ), we found a negligible bias (-0. 6%) and a RMSD of 15. 0%. Overall, both datasets are consistent and detect the major floodplains while CYGNSS shows a much higher sensitivity to small water bodies, especially around the Amazon and Congo tributaries. This is due to the higher native spatial resolution of CYGNSS coherent observations (∼1 × 6 km). Conversely, 𝑊 𝐹 𝐺𝐼𝐸𝑀𝑆 is higher along the Amazon mainstream and in Southeast and East Asia. High soil moisture consecutive to monsoons likely drives this clear overestimation (see also Fig. 11 ). Finally, it is worth noting a positive bias along the coastlines, either due to contamination from the ocean which can affect both datasets, or to the presence of mangroves not detected by GIEMS mostly due to its lower spatial resolution.
Regional comparisons A better evaluation of the performances of 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 and 𝑊 𝐹 𝐺𝐼𝐸𝑀𝑆 is conducted at the scale of the river basin. In Fig. 9 , both datasets are compared at low and high water stages with the inundation extent in the Amazon derived from JERS-1 (𝑊 𝐹 𝐽 𝐸𝑅𝑆 ) in 1995-1996  (Hess et al. , 2015). This comparison shows that most of the floodplains and river streams are delineated in 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 and the spatial correspondence with 𝑊 𝐹 𝐽 𝐸𝑅𝑆 is good. The comparison between 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 and 𝑊 𝐹 𝐺𝐼𝐸𝑀𝑆 shows a seasonal flood signal with similar amplitude, and a better level of detail in 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 due to its higher spatial resolution. Finally, 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 and 𝑊 𝐹 𝐺𝐼𝐸𝑀𝑆 are generally lower than 𝑊 𝐹 𝐽 𝐸𝑅𝑆. This bias is particularly noticeable at high water stage, along the Amazon stream and in the Llanos de Mojos (∼65 • W and 15 • S) in Bolivia.
Over the Cuvette Centrale of Congo, we used the wetland classification from  Betbeder et al. (2014)  to conduct a similar comparison which is shown in Figure  S2. 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 matches well the water fractions extracted from the PALSAR classification at low water stage, but is much lower at high water stage (December) when the Cuvette Centrale of Congo is almost entirely flooded according to the reference maps. Both 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 and 𝑊 𝐹 𝐺𝐼𝐸𝑀𝑆 only detect water along the streams of the Congo River and its main tributaries, where the longest floods occur. We also compared 𝑊 𝐹 𝐶𝑌 𝐺𝑁𝑆𝑆 and 𝑊 𝐹 𝐺𝐼𝐸𝑀𝑆 to MODIS WF estimations (𝑊 𝐹 𝑀𝑂𝐷𝐼𝑆 ) in the IND, the LMB and the Parana basins. Figure  S3  presents the annual maximum WF per pixel for all of the three datasets and the three study areas, with the associated surface water extent.