dataesr/scientific-paragraphs-categorization huggingface dataset:
Inundation Maps Derived from Landsat-8 Data Landsat-8 satellite collects visible and shortwave images (30 m spatial resolution). NIR wavelength reflects less solar radiation than the red wavelength over water bodies  [1, 3] , and surface water maps can be derived from the NDVI maps (water pixels and non-water pixels correspond to negative and positive values of NDVI, respectively)  [24, 25]. Other indices have been used to detect water, but the NDVI is effective when properly corrected from the atmospheric contamination. In this study, official and reliable atmospheric corrected Landsat-8 NDVI images are ordered directly from the U. S. Geological Survey (USGS) website (https://espa. cr. usgs. gov/index/). To limit cloud effects, only images with less than 10% of cloud contamination are used.
Inundation Maps Derived from MODIS/Terra Data In this study, the surface reflectance 8-Day L3 Global 500 m products from MODIS/Terra (MOD09A1) are used to create surface water maps, mainly based on values of the Enhanced Vegetation Index (EVI), the Land Surface Water Index (LSWI), and the difference between EVI and LSWI by a methodology described in  [14]. MODIS surface water maps (500 m spatial resolution) over the Mekong Delta will be used to compare to the corresponding surface water maps derived from SAR Sentinel-1 observations for 2015. MODIS/Terra data can be downloaded from http://reverb. echo. nasa. gov/reverb/. All Sentinel-1, Landsat-8 and MODIS/Terra observations used in this study are listed in Tables  1  and 2. Sentinel-1 and Landsat-8 training observations are used to train the NN (Section 3.
The threshold needs to be optimized in order to satisfy some quality criteria, such as overall accuracy or false alarm rates. The NN classifier needs to be trained in order to perform an optimal discrimination between water and non-water states. A supervised learning is chosen: the NN will be designed to reproduce an already existing classification. A dataset including a collection of SAR information x and associated surface water state y is first built. Part of it is then used during the training stage in order to determine the optimal parameters of the NN model. The reference dataset in the selected area is provided here by a Landsat-8 surface water map (NN outputs), in spatial and temporal coincidence with the Sentinel-1 SAR data (NN inputs). A maximum time difference of 3 days is tolerated, as the two satellites do not fly in phase.
Six Landsat-8 surface water maps are selected, along with the corresponding Sentinel-1 SAR observations (see Table  1  for more details on the training dataset). The selection process for the Landsat-8 images has been described in Section 2. 2. 1. The images cover parts of the lower Mekong Delta in Vietnam and Cambodia. For each image in the training dataset, the number of non-water pixels is much higher than the number of water pixels. To avoid giving too much weight to the non-water pixels, an equalization of the training dataset is performed: an equal number of non-water and water pixels is selected in the training dataset. For this purpose, non-water pixels are selected randomly in the images, to match the number of water pixels. The total number of training samples is ∼10 million pixels, half water pixels, half non-water pixels.
It takes ∼5 h to train the NN (with the use of a personal computer), but when the training is completed, a surface water map can be produced quickly (after ∼3-4 min) from any new set of satellite inputs x. A test dataset is chosen to measure the performance of the NN retrieval scheme with data not used in the training process. The NN methodology is summarized in Figure  5. Several tests were necessary to determine the optimum inputs to the NN, in addition to the obvious ones, i. e. , the backscatter coefficients for both polarizations. To limit ambiguities between flat arid surfaces and surface water, and to better capture small rivers, the spatial homogeneity of the backscatter coefficients appeared to be a relevant parameter. The standard deviation of the backscatter coefficients are computed locally over 100 m × 100 m boxes.
NN Sensitivity Tests In this section, we use a test dataset of three SAR Sentinel-1 images and three corresponding Landsat-8 reference surface water maps to make several sensitivity tests in order to optimize the performance of the NN classification (see details of the test data sets in Table  1 ). Three different sensitivity tests were carried out: (1) selecting the best threshold of the NN output to classify land/water surface; (2) understanding the effect of the equalization of the water and non-water pixels in the NN training dataset; (3) finding the most important satellite NN inputs. The NN performances have been evaluated based on: spatial correlation between the SAR and Landsat-8 surface water maps, overall accuracy of the NN, as well as higher values of true positive (TP) and true negative (TN) percentages.
Equalization of Water and Non-Water Pixel Number For this test, instead of using an equal number of water and non-water pixels in the training dataset, 10% of each Sentinel-1 image is selected randomly to train the neural network, meaning that the number of non-water pixels is several times higher (10-15 times depending on each image in the training dataset) than the number of water pixels (as seen in Figure  7 ). The intersection between histograms of the NN outputs for water pixels (blue) and non-water pixels (red) moves to 0. 5 (see the histogram in Figure  8 ), meaning that the value 0. 5 should be selected to separate water from non-water clusters. As shown in Table  4 , the resulting NN is very efficient at detecting non-water pixels with a true negative detection of 99. 7%, but it misses 14% of the actual water pixels (86% of true positive detection only, compared to 91% with the equalized training dataset-Table  3 ).
Analyzing the Weight of Each NN Satellite Input To identify the most relevant inputs for the NN classification of the water surface, 15 NNs are trained based on all 15 different combinations of five input parameters, and their performances are evaluated following various criteria. Table  5  presents the best results with one to five inputs and illustrates how the overall accuracy of the NN classification increases when the number of satellite inputs increases, as compared to the reference Landsat-8 dataset. The NN trained with only the VH backscatter coefficient has a spatial correlation of 78% and a true positive accuracy (correctly detecting water pixels) of 77% compared to the reference data. The spatial correlation increases to 79%, and the true positive accuracy rises to 85% when the standard deviation of the VV backscatter coefficient is added as an input to the NN.
An equal number of water and non-water pixels should be used in the training dataset to ensure that the NN performs equally well in classifying water and non-water clusters. The STD_VH provides limited additional information to the NN due to its strong linear correlations with the other NN inputs. The incidence angle also plays a limited role in the NN performance. This is partly due to the rather narrow range of incidence angle, from 29 • to 46 •.
Results and Comparisons with Other Surface Water Products The following results and comparisons involve the optimized version of the NN classification with five input parameters (an equalization of water and non-water pixels, and the output threshold is 0. 85). In Section 4. 1, the SAR-predicted surface water maps are calculated for two test areas in the Mekong Delta, and compared to Landsat-8 surface water maps over the Tonle Sap Lake in Cambodia and over the Mekong river in Vietnam (see test dataset in Table  1 ). Other regions were tested but the results are not shown here. Due to the lack of in-situ local inundation maps at the time of this study, we do not have a reference dataset to confirm the accuracy of the Landsat-8 based maps. Therefore, an inter-comparison between Sentinel-1 estimate and other existing estimates is the only way to evaluate the new wetland product based on SAR Sentinel-1 data.
First, the results are evaluated with respect to the floodability map derived mainly from the HydroSHEDS topography dataset  [33] , developed by  [34]  (Section 4. 2). Second, time series of the SAR-derived surface water over the Mekong Delta is compared to the MODIS/Terra-derived inundation maps based on the methodology described by  [14] , for 2015 (Section 4. 3).
The overall accuracy is 98. 8%, with a spatial correlation of nearly 82% with the Landsat-8 reference surface water map. Confusion matrix for this area is shown in Table  7  (right) where the true positive percentage is 85. 7%, the false negative percentage 14. 3%, and 99. 2% of non-water pixels are classified correctly. The total surface water area derived from Landsat data is 325 km 2 , and it is 355 km 2 predicted from the NN. The same results are found when applying the NN classification to other areas. To conclude this comparison, the proposed NN methodology correctly detected ∼90% of the water pixels observed by Landsat-8, with a spatial correlation of ∼90%. The NN works better over open water bodies than over other heterogeneous environments. For instance, the NN has difficulties detecting small river branches (Southeast of the Tonle Sap Lake in Figure  9 -top panel) although they are clearly detected with Landsat-8 images.
Evaluation Using a Topography-Based Floodability Index A global floodability index based on topography has been developed by  [34]. It uses mainly the Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales (HydroSHEDS) dataset  [33]  that has been derived from elevation measured by the Shuttle Radar Topography Mission (SRTM) satellite. This floodability index provides a static map of an estimate of the probability for a pixel to be inundated (between 0% and 100%) at the spatial resolution of 90 m, based only on topography information (such as slope in the pixel, distance to the closest river, difference of elevation with the closest river). Figure  10a  presents this floodability index map over the whole Mekong Delta. As expected, all rivers and lakes in this area have a very high probability of being inundated (over 80%).
Since this index is based only on topography, its reliability is higher for natural environments and it can be less precise over regions with strong anthropic impact such as irrigated areas. The floodability data is upscaled from 90 m to 30 m spatial resolution to compare with predicted SAR surface water maps over the Tonle Sap Lake and the Vietnamese Mekong Delta. Each floodability pixel is divided into a 3 × 3 matrix with the same value, and projected onto the Sentinel-1 grid. By comparing these two products, we can see where and how Sentinel-1 water pixels are located with respect to the floodability index, and test the consistency between two independent products. Figure  10b-e  show floodability maps at 30 m spatial resolution and predicted Sentinel-1 water maps, over four different areas in the Mekong Delta.
For the second assumption (the surface water extent of a mixed pixel is increased to 50%), the difference in surface water areas increases (without significant changes in the seasonal cycle with still high correlation with the SAR surface water time series). With both hypotheses, the SAR and MODIS surface water extents reach their maximum at the same time (around 20 October 2015). Total inundated areas derived from SAR and MODIS are very close during the dry season (January to July). The cloud contamination of the MODIS estimate is low during that season. During the rainy season, more cloud contamination is expected in the MODIS estimates, and the discrepancies between the two surface water extents increase. The SAR-derived surface water estimate is expected to be more reliable due to its insensitivity to the cloud cover, but at this stage there is no convincing dataset at this spatial resolution to confirm it, as mentioned before.
Conclusions and Perspectives This study presents a methodology to monitor and quantify surface water under all weather conditions within Cambodia and the Mekong Delta in Vietnam, using high quality Sentinel-1 SAR observations, freely available online. The methodology is based on a neural network classification trained with optical Landsat-8 images at 30 m spatial resolution. The information content of each satellite input is analyzed and the inputs are selected to optimize the performance of the classification. This method allows the detection of surface water with good accuracy when compared to visible and NIR data under clear sky conditions, as well as when compared to a floodability map derived from topography data. Surface water maps derived from the proposed NN show a spatial correlation of ∼90% when compared to Landsat-8 water maps, with a true positive water detection of ∼90%.
Acknowledgments: We would like to thank  Toshihiro Sakamoto  for proving the methodology to derive the MODIS 500 m surface water maps. We are thankful to  Simon Munier  for interesting discussions and for sharing some of his tools in the framework of his ESA-funded postdoc ( CCI Living Planet Fellowship   AO/1-7829/14/I-MB ). We also thank  Thuy Le Toan ,  Stéphane Jacquemoud ,  Thanh Ngo Duc , and  Nicolas Delbart  for interesting discussions and suggestions for this work. This study was financially supported by a PhD fellowship from the  Vietnam International Education Development (911  project). We would like to thank three anonymous reviewers for their helpful comments and suggestions that helped to improve the quality of this manuscript.
U sing the communications antenna aboard the NASA Dawn spacecraft, we conducted the first orbital bistatic radar (BSR) observations of a small body at asteroid Vesta at grazing incidence angles during entry and exit from occultations. In this configuration, Dawn's high-gain telecommunications antenna (HGA) transmitted X-band radio waves during its orbit of Vesta, while the Deep Space Network (DSN) 70-meter antennas received the signal on Earth. Dawn's orbital trajectory was designed to ensure that the spacecraft's HGA communications antenna would almost constantly be in the line of sight with ground stations on Earth 1 , but on occasion, the spacecraft inevitably passed into occultation behind Vesta-lasting as briefly as 5 min or as long as 33 min. By continuously transmitting basic telemetry data from Dawn's antennas during these events, the opportunity arose to observe surface reflections of HGAtransmitted radar waves from Vesta's surface.
In all, five entries and nine exits passed our criteria for analysis and measurement of σ (km 2 ) and σ/σ max (dB). The following sections describe the method used to process and analyze the resulting BSR data. Supplementary Table  1  summarizes the acquisition parameters of the BSR experiment. Processing DSN BSR data. Radio waves received by the DSN 70-meter antennas are recorded as amplitude (voltage) versus time, and are collected in two channels: RCP and LCP. Within each channel, amplitude data is recorded in two components, in-phase I and quadrature Q, which correspond to the real and imaginary parts of the complex voltage, respectively. The raw modulated telemetry data collected at the DSN antenna is sampled at a rate of 16 kHz. In addition, a Doppler shift correction is applied to the X-band receiving frequency in order to counteract the calculated Doppler shift induced by Dawn's orbit around Vesta.
5°by 11. 5°grid (~51 × 51 km) centered on each echo site's coordinates, and must be sufficiently smaller than the incident 11-km beam diameter. Calculated h rms range from 0. 002 to 0. 13 km. All but one echo site had h rms < 0. 11 km (i. e. , topographic variability of 1% of the incident beam diameter). The echo site of occultation entry during orbit 377 exceeded this criteria and is therefore excluded from the following analyses. Since HGA transmissions are measured continuously throughout the minutes that precede and follow an occultation of the spacecraft behind Vesta, only engineering data is included in the raw telemetry, and DSN receiver calibration measurements are not included with the raw radar data set. Because the BSR data are not calibrated in absolute voltage, calculating σ therefore requires calibrating the measured power to a known reference.
P r dir|meas is evaluated by measuring the area under the curve in non-logarithmic units during a time when the direct signal is not obstructed by Vesta. Since the data is discrete, P r dir|meas is the sum of the power in each frequency bin multiplied by the width of each frequency bin (subtracted by the noise power in the same bandwidth): P r dirjmeas ¼ X P i Á Δf step À P N Á f BW ð4Þ where frequency step Δf step is the spectral resolution of ~0. 4 Hz, as previously determined when calculating the differential Doppler shift; P i is the nonlogarithmic power in data units of each discrete point measured within a 10-Hz bandwidth (f BW ) of the direct signal peak; and P N is the average noise power (data units Hz -1 ) in the spectrum. The conversion factor between watts and power measured from BSR data units is therefore: C ToWatts ¼ P r dir calc j Watts ½ P r dir meas j Data Units ½ ð5Þ and the BSR equation, solved for σ, is then: σ m 2 ð Þ ¼ 4π ð Þ 3 R t 2 R r 2 L G t G r λ 2 P r echojmeas 1 À X Pt ð Þ P t ð6Þ where X Pt is the fraction of incident power that has been reduced due to partial obstruction of the HGA beam by Vesta's surface, and P r echo|meas (W) = P r echo|meas (Data Units) × C ToWatts.
Acknowledgements The authors thank the  NASA JPL Radar Tracking Group  for acquiring Dawn's BSR data, with special thanks to  Daniel Kahan  and  Sami Asmar  for many helpful discussions about the resulting data product. Dr Mahta Moghaddam  (USC) is greatly appreciated for sharing her expertise in BSR surface scattering during the final stages of the manuscript. We also thank  Dr Steven Joy  ( UCLA ) for providing us with ephemeris data from SPICE kernels and  Dawn PI Dr Christopher Russell  ( UCLA ) for enabling this experiment. Lastly, we thank  Deputy PI Dr Carol Raymond  (JPL) and the  Dawn Operations Team  for their support throughout the experiment. Palmer was previously funded as a graduate student by  UCLA , the  California Institute of Technology  and  Western Michigan University. This work is currently funded through  USC  under the  NASA   Planetary Geology and Geophysics program  ( WBS 811073.
The initial source was positioned 270 m from the first geophone, with multiple shots stacked to produce an acceptable signal-to-noise in the recordings. The numbers beneath each source location in Figure  1B  indicates the number of shots stacked at each source. The number of shots was determined by experimentation in the field and designed to equalize data quality across different hammer operator abilities and endurance, and to optimize the unfiltered signal-to-noise quality of the seismograms (e. g. , Figure  2 ). The final dataset consisted of operators stacking over 7000 shots, 18 separate seismic lines, and 21,312 individual seismogram recordings from the geophone stations.
Seismograms were visually examined to select the onset travel times of the refracted P-waves arrivals and determine the underlying seismic velocities. Each seismogram was Butterworth band-pass filtered between 40 and 200 Hz. This filtering range was chosen to remove high and low frequency environmental noise and isolate the seismic arrivals. Each P-wave arrival was selected manually according to the first coherent swing deviating from zero amplitude. Consistency between picks was achieved by examining all data for a shot simultaneously and picking on the same polarity across all seismograms (Figure  2 ). The picking error was defined by pixel accuracy of our picking tool (2-3 pixels), and was less than 0. 5 ms.
Inversion for Ice Seismic Velocity Models To determine the seismic structure of the firn and aquifer, we invert the travel time picks of the continuously refracted waves for one-dimensional (1D) velocity models. Past active source surveys of glacial ice have fit refraction data with an exponential function (e. g. ,  Kirchner and Bentley, 1979). In this study, sharp increases in velocity from the top and bottom of the firn aquifer were anticipated, so we chose to represent the glacier with constant velocity layers in depth (e. g. ,  Shearer, 2009). A further assumption is that the first arriving refracted waves are insensitive to decreases in velocity. In effect, the data could be equally well fit by models with an arbitrary number of low velocity layers. We therefore look for models with velocity increasing with depth.
DISCUSSION Effective media estimations require knowledge of the matrix and fluid velocities to determine the bulk velocity of a fluidfilled medium (e. g. ,  Biot, 1956; Wyllie et al. , 1956 ). Here we assign a value of 1450 m s -1 for liquid water and 343 m s -1 for air  (Shearer, 2009 ). An assembled dataset of laboratory and in situ measurements of firn and ice velocity presented in  Kohnen (1974)  show that polycrystalline ice at 0 • C should have a seismic velocity of 3795 m s -1. This is comparable, given the uncertainties, to the highest velocities observed in our study region in 2015, 3883 ± 165 m s -1 at site 10 days; in 2016, 3751 ± 20 m s -1 at site 8a. However, the majority of sites across our study region are systematically slower than this expected value for ice, up to 10% less at Site 1.
ACKNOWLEDGMENTS We thank  Kyli Cosper  and the entire  CH2MHILL PFS Team for excellent field logistics  and support. We thank our two review editors for providing thorough and insight commentary that helped to greatly improve the quality of the manuscript. Seismic instruments were provided by the  Incorporated Research Institutions for Seismology (IRIS)  through the  PASSCAL Instrument Center at New Mexico Tech. Seismic data collected will be made available through the IRIS Data Management Center as an assembled data set. GPR data will be made available through the Arctic Data Center. The facilities of the  IRIS Consortium  are supported by the  National Science Foundation  under Cooperative Agreement  EAR-1261681  and the  DOE National Nuclear Security Administration. Figures in this manuscript were constructed using the Generic Mapping Toolkit (Wessel et al.
Moreover, molecules, isotopologues, and perturbing gases have been added that address the issues of atmospheres beyond the Earth. Of considerable note, experimental IR cross-sections for almost 300 additional molecules important in different areas of atmospheric science have been added to the database. The compilation can be accessed through www. hitran. org. Most of the HITRAN data have now been cast into an underlying relational database structure that offers many advantages over the long-standing sequential text-based structure. The new structure empowers the user in many ways. It enables the incorporation of an extended set of fundamental parameters per transition, sophisticated line-shape formalisms, easy user-defined output formats, and very convenient searching, filtering, and plotting of data.