dataesr/scientific-paragraphs-categorization huggingface dataset:
[23]  developed the Global Surface Water (GSW) dataset harnessing Google Earth Engines' (GEE)  [24]  capacity to produce not only a global water map, but also a database of water dynamics over 1984 to 2019 at a 30 m spatial resolution and a monthly scale. This can notably be combined with satellite altimetry data (e. g. , DAHITI  [25] ) to produce global lake and reservoir volume estimates. Donchyts et al. [26]  produced a similar tool (Deltares Aqua Monitor) based on Landsat imagery at a 30 m resolution, while Yang et al. [27]  recently used Level 1C Sentinel-2 imagery in GEE to map surface water dynamics across the whole of France. Despite the vast opportunities provided by these global datasets, research has also shown the limitations of these works in specific contexts. Optical sensors such as those aboard Landsat satellites are affected by cloud cover, leading to incomplete representation of seasonal wetlands and inundation patterns  [28].
To overcome these limitations,  Yao et al. [29]  used Landsat imagery since 1992 and enhanced cloud corrections to increase the number of available observations and create a long-term high frequency time series. Based on multiple sensors, including active sensors, a global database of surface water extent over 12 years (Global Inundation Extent from Multi-Satellites-GIEMS) was developed at a 0. 25 • (around 27 km) resolution, subsequently downscaled to 500 m based on ancillary data  [30] [31] [32]. Combining optical and radar imagery can notably improve observations during the rainy season when cloud obstructions interfere with reflectance values of passive sensors  [33]. Similarly, a rising number of works seek to combine the unparalleled long-term observations from Landsat (launched in 1972) with the recent advances from Sentinel-2 imagery  [34, 35] , and NASA has distributed Harmonized Landsat and Sentinel-2 surface reflectance datasets at a 30 m resolution since 2013  [36].
However, combining and fusing satellite sources require understanding the relative benefits of these observations including in long-term studies to guide their selection and combination  [43, 44]. The need for consistent long-term datasets to monitor land use also introduces further constraints to classify data and threshold indices in automated, but consistent ways  [42, 45, 46] , whose accuracy must be assessed and optimised against ground truth data. This paper seeks to evaluate and compare the relative benefits of combining multiple imagery sources to monitor surface water areas in floodplains, characterised by temporary inundation and heterogeneous water environments. Extensive and high accuracy field monitoring of surface water over 1999-2019 in the Senegal River floodplain is used to quantify the capacity and accuracy of four major sources of Earth observation to monitor long-term surface water variations.
Tiles from path 204 and row 49 were used here. Our region of interest overlaps on row 48; however, there is no benefit from combining both observations as rows are captured in close succession. The minor delay between rows 48 and 49 can change cloud presence, but only marginally, as observed here. Where the region of interest overlaps on two paths, this can result in a greater frequency of observations  [7]. Images provided by the U. S. Geological Survey (USGS) ESPA are processed to surface reflectance (Level 2A) using the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) for sensors TM and ETM+ and the Landsat Surface Reflectance Code (LaSRC) for the OLI sensor. These surface reflectance products are available directly from USGS data centres such as Earth Explorer and through Google Earth Engines (assets LANDSAT/LE07/C01/T1_SR and LANDSAT/LC08/C01/T1_SR).
MODIS Data Nine-hundred thirteen images from the MODIS sensor aboard the Terra satellite were used. With its wide swath, this medium resolution sensor benefits from shorter recurrence periods and provides daily coverage of many parts of the globe. Multispectral imagery is captured at 250 m in 2 bands, 500 m in 5 bands in the visible and the infrared spectrum and at 1000 m in the other 29 bands. The 6th version of the MOD09A1 surface reflectance products at 500 m spatial resolution was accessed via Google Earth Engines (asset MODIS/006/MOD09A1). Each pixel is selected within an 8 day window to provide a composite image with the best observation in terms of low viewing angle, reduced clouds and cloud shadow presence and minimal aerosol loading. Images provided in sinusoidal format were reprojected to the Universal Transverse Mercator system.
Sentinel-2 Data Two-hundred thirty-five images from European Space Agency's (ESA) constellation of Sentinel-2 satellites were used. Sentinel-2A and Sentinel-2B have provided multispectral imagery in the 10 m and 20 m bands since 23 June 2015 and 7 March 2017, respectively, over our region of interest. The satellites have a 10 day revisit frequency offset by 5 days, leading to image availability every 5 days since 2017 when combining both sensors. Images are available directly from the Copernicus Scihub, and Level 1C images have been fully integrated into GEE (asset COPERNICUS/S2). Level 2 surface reflectance products have recently been integrated into GEE, based on radiometric and atmospheric corrections performed using the Sen2cor algorithm  [50]. However, these are not available before 28 March 2017 in GEE and not before 16 December 2018 for our region of interest.
Cloud Detection Images within Google Earth Engines are provided with cloud and shadow masks. On Landsat image collections, cloud and shadow flags populated with information generated by CFmask is provided in Bits 3 and 5 of the attached Pixel Quality Assurance (pixel_qa) band. This is complementary to the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) and Land Surface Reflectance Code (LaSRC) based (sr_cloud_qa) bands. CFmask is a C-based adaptation of the Function of mask  [51]  developed for Landsat and adopted by USGS, shown to perform better than the initial LaSRC and LEDAPS internal tests and the Automatic Cloud Cover Assessment (ACCA) algorithm  [52]. On the Landsat 8 OLI sensor, cirrus cloud detection is also improved thanks to the presence of the additional Band 9.
Based on the interpretation of pixel_qa values defined in USGS handbooks  [53, 54] , the clouds and shadow pixels and their corresponding percentage were evaluated over the region of interest. On MODIS, the StateQA band provided as part of the MODIS Science Data Sets features flags related to cloud and cloud shadow presence on each pixel  [55]. Bits 0, 1 and 2 provide information on clouds and cloud shadows. These were interpreted in GEE to quantify the number of cloud and shadow pixels over the region of interest on each of the 913 images. On Sentinel-2, Bits 10 and 11 on the QA60 bitmask band provide information on the clouds, shadows and cirrus on each pixel. All available images were processed in GEE, considering the uncertainties from using cloud filters based on inadequate cloud detection.
e. , as here, pixels with a low and high MNDWI value corresponding to water pixels and bare soil pixels. To improve its performance here, region growing is implemented manually by enlarging the sampling area to include the Senegal River when calculating the histogram. This ensures a minimal constant fraction of water pixels at all stages and a greater variance in MNDWI pixel values. 3. The third is data intensive site-specific thresholds calibrated against ground truth observations (Tcalib). A split sample approach was used: the 2018 flood was used to calibrate thresholds, and the corresponding threshold was applied on the 2017 and 2019 floods in the validation phase. The MNDWI threshold for imagery from each satellite was determined based on maximising overall accuracy across all images of the 2018 flood.
Ground Truth Data over 2017-2019 Stage values were monitored during the 2017-2019 floods using a limnimeter installed in the floodplain, providing automatic readings every hour between August and December each year (234 days). The on-site instrumentation consisted of a Hobo pressure transducer in the lowest part of the Podor basin providing measurements of the absolute pressure, which is a combination of the water head and atmospheric pressure. A secondary Hobo pressure installed in the town of Podor was used to measure atmospheric (barometric) pressure and compensate readings from the 1st sensor to provide highly accurate measurements of the water head. Line levelling using a Leica Geosystems level and a known geodetic marker at the Podor quay used by the river basin authority (Organisation pour la Mise en Valeur du Fleuve Sénégal (OMVS)) was undertaken to determine the absolute height of water in the orthometric Nivellement Général d'Afrique de l'Ouest (NGAO53) datum.
High Precision UAV Data Unmanned aerial vehicle (UAV or drone) mapping of flooded areas was carried out to provide external validation of the flood maps generated from the DTM and stage monitoring. Very high resolution imagery of the flooded area was acquired with a DJI Phantom 4 UAV. Fifteen flight paths based on the location of flooded areas, wind direction, access possibilities and overlap requirements were pre-defined in Litchi Mission Hub (https://flylitchi. com/hub). A 60% side overlap and 80% forward overlap were used. The drone was flown during clear conditions on the 12 October 2018 close to the flood peak. The area was surveyed at a 300 m altitude, resulting in a ground sampling distance of 13 cm. Other characteristics are provided in Table  2. The resulting 507 georeferenced images were processed in a professional image-based 3D modeller software, Agisoft Photoscan software, known to perform well  [70].
The software uses the recorded Global Navigation Satellite System (GNSS) positions and Inertial Measurement Unit (IMU) data and aligns images through a feature matching algorithm, which detects and matches stable features across images. Then, the 218,736 tie points between overlapping images were used to generate a dense point cloud model and the three-dimensional polygonal mesh, leading to the final orthomosaic image  [71]. Root mean squared reprojection error was low at 1. 5 pixels. Further details on the algorithms and principles used by Agisoft Photoscan can be found in Gonçalves and Henriques  [72]. Flooded areas were delineated using a random forest classification in GEE. Random forest has notably been shown to perform well with UAV red-green-blue imagery  [73, 74]  and in various hydrological applications  [75].
Threshold Calibration and Pixel-Based Accuracy Assessments For Tcalib, MNDWI rasters for 40 Landsat, Sentinel-2 and MODIS images over the 2018 flood were exported from GEE, cropped to the region covered by the DTM. Binary rasters for each MNDWI threshold value between -1 and 1 in 0. 01 increments on each image of 2018 (i. e. , 8000 rasters) were created, and confusion matrices with the corresponding ground truth flood map were calculated. Optimal thresholds were determined based on maximising the overall accuracy. The inter-image optimal threshold was then determined based on summing correctly and incorrectly classified pixels across all images during 2018 and an objective function to maximise overall accuracy for each satellite source. A final script for Tcalib extracted the flooded areas and classified MNDWI rasters for 2017 and 2019 based on the optimal threshold for each satellite.
Comparison with Global Surface Water Datasets The MNDWI classifications obtained through our approach were then compared with the published Global Surface Water datasets produced by Pekel et al. [23]  and available within Google Earth Engines. These exploit the Landsat 5, 7 and 8 archives to provide, based on a combination of expert systems, visual analytics and evidential reasoning, what is often regarded as the most advanced and detailed surface water datasets at a 30 m resolution  [42]. All available (246) images from the GSW datasets for 1999-2019 were treated in GEE to extract monthly surface water values for our region of interest. The accuracy of these individual observations, as well as the resulting interpolated time series were compared and quantified in terms of NSE and RMSE, against the ground truth surface area time series and the outputs from the sensor and multi-sensor time series generated through our approach.
On Landsat 7 with T0, omissions errors are contained to 11% on average. During high waters, the very small water channels in the SE of the image are not reconstituted correctly by the gap fill approach of Landsat 7. The overall performance remains remarkably good, and with all three methods, mean MAE remains between 6 and 7% and below 1,000,000 m 2. The results therefore highlight differences in the suitability of each method on each satellite sensor. On MODIS, calibrating a site-specific, constant low threshold is clearly beneficial, producing the lowest MAE, 53% less than with TOtsu. For Landsat 7, a default threshold of zero produces the best results. For Landsat 8, a dynamic threshold method such as Otsu leads to the lowest errors, and more intensive methods such as Tcalib, which require ground truth data (classified image, DTM, DGPS contours, etc.
Results also highlight the reduced performance from the GSW datasets, where RMSE reaches 1,340,000 m 2 (Table  7 ) and often fails to capture the flood peak. The value of Sentinel-2A and -2B five day repetitivity over Landsat 8 is clearly shown in Figure  12 , where for instance in October 2018, Landsat 8 underestimates the flood peak due to insufficient observations, irrespective of what classification method is used. In earlier years, Landsat 7 suffers from its temporal resolution and yields numerous gaps, which result in missing peaks (e. g. , 2003) or interpolating over several weeks (e. g. , 2012). Figure  11  illustrates this reduced number of observations in the middle range of values, partly due to clouds during the rise of the flood, and the rapid rise and decline phases observed.
Previous research notably showed that MODIS was suited to the study of hydrological systems superior to 10,000 km 2  [8]. Khandelwal et al. [17]  using a supervised classification algorithm based on Support Vector Machines (SVMs) showed that MODIS imagery could be used to study water trends on large lakes ranging between 240 km 2 and 5380 km 2. Our results show that site-specific thresholds on MNDWI can be used to monitor daily long-term variations of flood dynamics under 19 km 2 and reduce the RMSE by 50% compared to Otsu segmentation. Field calibrated thresholds are shown to be most suited with MODIS to force a lower threshold and take into account the presence of shallow waters and flooded vegetation in the pixels, supporting results on other wetlands  [12]. Global Surface Water datasets  [23]  are often referred to as the best attempt to provide water maps at high resolution  [42]  and many global datasets are developed solely on Landsat and Sentinel archives  [22, 23, 26, 36].
The results demonstrate the clear limitations before 2013 of GSW datasets built on one image every 30 days, to monitor flood dynamics in floodplains. These are notably insufficient to reproduce surface water dynamics in the smallest (<1 km 2 ), temporary water bodies with flash floods  [7, 82, 83]  and are also insufficient in floodplains with slow flood dynamics. Using the full Landsat archive provides observations every 24 days on average over 1999-2019, but the benefits of integrating additional data sources remain indisputable, e. g. , to capture flood peaks in 2003 and 2004 missed here by Landsat. Despite cloud presence during the flood rise, optical imagery is shown here to be sufficient to monitor surface water variations. Combining or fusing optical imagery with active radar imagery such as Sentinel-1 may be relevant in areas or periods where cloud presence is more frequent and problematic  [84] , notably during the monsoon season at tropical latitudes.
The GSW datasets based on machine learning systems have also been shown to perform less well in mixed water environments found in small water bodies and wetlands  [7, 83]. In parallel, these results highlight the value of long-term hydrological observations to assess the accuracy and skill of remote sensing datasets. Remote sensing studies often rely on alternate high resolution satellite imagery or, where possible, in situ observations  [90] , such as geolocalised sampling using GPS points, transects and contours to train and evaluate classifications. Validation is often based on random sampling of water bodies, but is restricted, for reasons of cost or feasibility, in the number of observations over time and rarely informs about the suitability to reproduce daily water dynamics in long-term monitoring  [7, 42, 91].
Many global water datasets are similarly developed against ground truth data for open water bodies, which as discussed above, can reduce their value in wetlands and other heterogeneous water environments. Long-term hydrological observatories, supported by citizen science observations  [92] , must then be encouraged to support the development and assessment of remote sensing products and algorithms relevant to the water community. New products such as higher accuracy DEMs from Tandem-X  [3]  or localised UAV-based methods [93] also have the potential to provide valuable ground truth data. Finally, our results point to the importance of using multiple metrics in classification accuracy assessments. Overall accuracy can lead to difficult or incomplete interpretation  [77]  of classifications, due to the effects of class prevalence  [78] , i.
The constellation of Sentinel-2A and 2B satellites offering high spatial and temporal resolution performs well in surface water monitoring, and integrating MODIS observations degrades overall accuracy. The integration of Landsat and Sentinel-2 yields modest improvements. This has important implications when developing long-term consolidated datasets designed to be used by researchers and stakeholders to understand flood variations, water balance modelling and water availability of wetlands and floodplains. The frequent surface water maps produced by this semi-automated multi-sensor approach may be used to understand how water overflows into the floodplain and improve hydrological representations of hysteresis as water recedes. Supported by Google Earth Engines' geoprocessing capacities, this approach can be applied across entire wetlands and floodplains to provide high resolution maps of flooded areas, calibrate hydrological models, improve local digital elevation models and investigate relationships with agricultural practices.
Forcing data and model initialisation All model pools were spun-up to equilibrium forced by preindustrial conditions. This comprised of a constant atmospheric CO 2 concentration of 287. 14 ppm, cycling global climate data at 0. 5 • × 0. 5 • resolution for the years 1901-1930 from the CRU-NCEP dataset version 7. 0  (New et al. , 2000) , using constant 1860 land cover from the  Hurtt et al. (2020)  database, and 1860s nitrogen deposition from the Atmospheric Chemistry and Climate Model Intercomparison Project  (Lamarque et al. , 2013). Next, transient historical runs were performed for the 1861-1900 period with the same climate forcing as the spin-up but with time-varying atmospheric CO 2 concentrations from synthesised ice core and National Oceanic and Atmospheric Administration (NOAA) measurements, as well as annually varying land use from  Hurtt et al.
, 2017). The hypothesis of no pre-industrial N limitation is based on the assumption that, prior to industrial times, the conditions of natural terrestrial ecosystems were stable for sufficient time to permit any lack of N availability to be filled by BNF  (Thomas et al. , 2015). Consequently, the pre-industrial Control run with both N and C is very similar to the C-cycleonly version, and a C equilibrium is reached before a N equilibrium. The disjoint between the C and N equilibriums may lead to varying levels of simulated N availability and may affect the model responses to perturbations. While there is evidence for wide-spread (co-)limitation of NPP in recent decades  (LeBauer and Treseder, 2008; Song et al. , 2019; Vitousek and Howarth, 1991) , there is insufficient data to test the hypothesis of no pre-industrial N limitation.
It has been shown elsewhere that LPJ-GUESS  (Wårlind et al. , 2014)  and BIOME-BGC  (Churkina et al. , 2009)  have a significant non-linear (synergetic) term between CO 2 and N deposition. An assessment of the combined effects of +N and +CO 2 may show a significantly different picture of model performance. Part of the uncertainty in the models comes from the reanalysis climate dataset used to drive the models. CRU-NCEP was chosen for the good spatial and temporal coverage, but some biases exist in the data compared to climatologies such as WATCH  (Weedon et al. , 2011). Offline simulations driven by low forcing frequency (6 hourly) CRU-NCEP data significantly overestimate evapotranspiration in regions with convective rainfall types and thereby could affect stomatal conductance and photosynthesis  (Fan et al.
Lawrence et al. (2019). :Nitrogencycling in CMIP6 land surface models to be sensitive to the driving climatology. JSBACH, JULES-ES, and LPJ-GUESS may be particularly strongly affected due to their dynamic vegetation. Lawrence et al. (2019)show that CLM5 corresponds best to benchmarks with the GSWP3 forcing dataset(van den Hurk et al. , 2016), and work with JULES shows that climate forcing is the biggest cause of variance of those considered.
" 51 Canioni, L'Histoire extraordinaire d'Hélène Parsi. Another example is the French stigmatic who was tried in France and Italy, the antipope Clément XV  (Michel Collin, 1905 -1974) , but he was accused of being a swindle in his "Petit Vatican" in Clémery, and for insulting Pope Paul VI. See Delestre, Clément XV, 92. For information on this and other cases, see the keyword "lawsuit" in the Stigmatics database. 52 In the 1880s, the German woman, Sabine Schäfer, was imprisoned for ten weeks in Baden after a bottle of blood and an instrument that could produce the wounds was found under her bed. Another interesting trial is that of Bertha Mrazek (1890-1967, a. k. a. Georges Marasco), a Belgian stigmatic and miraculée who was tried in 1924 for obtaining money under false pretences and was sent to an asylum.
In this case, however, it is not clear if the holy wounds were addressed in the trial, as no records survive. See biographies in the Stigmatics database and the appendix; as well as Thurston, Surprising mystics, 210-213; and Van Osselaer, "The many lives of Bertha, Georges and Jean. " 53 See Catherine Filljung, VI-Q-62bis, Archives de la Province Dominicaine de France (APDF), Bibliothèque du Saulchoir. For a biography favourable to Catherine Filljung, see Ebel, Soeur Catherine  (1929). For an unfavourable one, see Pelt, La vérité sur Catherine Filljung  (1934). The lawsuits confirmed their political symbolism from the moment they were initiated. In the Spanish case, the liberal governor of Madrid decided to initiate the lawsuit, while in the French case, it was the public prosecutor (procureur de la République) of Apt.
Thus measurements of c p in the spectral region around 676 nm contain information that is directly related to Chl-a. However, this information is obscured by other variable factors including the concentration, composition and size distribution of all the other particles suspended in the water that generate a c p signal. As a consequence, only very recently has the estimation of Chl-a from c p been attempted  [21]. Here, we hypothesised that, with a dataset large enough to be representative of a wide range of Chl-a values and optical conditions, a machine-learning algorithm could be trained to estimate Chl-a from c p spectra. Luckily, tens of thousands of particulate absorption and attenuation measurements have been collected in the surface ocean in underway mode  [22]  in many ocean regions  [9, 10, [12] [13] [14] 23, 24].