dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions The recent increase in the number of Earth Observation satellites has expanded the use of such data in characterising earthquake deformation. The large data volumes and an open data policy of the Sentinel missions have been an important development for accessibility, and this change has democratised the application and exploitation of such datasets. It has widened the uptake from users for both scientific and commercial use, and it has increased the range of scientific questions that can be addressed, as well as the number of environmental, commercial and industrial applications. Here we have selected a few examples where the data coverage, quality and availability have been optimal to make for good case studies. However, sometimes there is a suboptimal response to an earthquake in terms of coverage for the longer-term postseismic phase, as well as a lack of regular data for a complete time series to look at long-term deformation processes for slowly straining areas.
Whilst the earthquake itself did not pose a particular major shaking hazard to the region, it illuminates a potential source fault in an area that previously contained only a few identified active fault traces. Although ambiguities in interpreting remotely derived datasets still remain, such observations provide an impetus to carefully study an area in terms of the active tectonics. Combining InSAR observations with remotely derived high-resolution topography will continue to improve our assessment for the potential of earthquake shaking near cities. We also described examples of making surface observations using pixel offsets from both optical EO systems and from SAR using the technique of sub-pixel cross-correlation. This method is complementary to InSAR and is particularly suited to observing large offsets associated with major surface rupturing continental earthquakes.
Using ever-increasing high-resolution imagery from optical satellites, it is possible to provide huge amounts of detail of the fault rupture at the surface. Important questions of the amount of on and off fault deformation can start to be addressed, as well as to determine the differing behaviour of the crust in terms of elastic and non-elastic behaviour  (Diederichs et al. 2019). Finally, we illustrated the importance of combining spacebased EO with other airborne remotely derived datasets using an example of triggered seismicity from Konya, India. Using a single source of remotely sensed data is unlikely to give the complete overview of a geophysical process; as is the case here in understanding the impact of reservoir loading on the evolution of seismicity. The importance of a complete synthesis is highlighted for determining the controls and interaction of the subsurface geology and surface loads with the locations and rates of subsequent seismicity.
Acknowledgements This work contains modified Copernicus Sentinel data 2019. Original SAR data is available to download from https ://scihu b. coper nicus. eu/. Geodetic Bayesian Inversion Software (GBIS) used in the modelling of InSAR data is available here: https ://comet. nerc. ac. uk/gbis/. John Elliott  acknowledges support from the  Royal Society  through a  University Research Fellowship  ( UF150282 ). This work was supported by  NERC  through the Looking into the Continents from  Space (LiCS)   large Grant  ( NE/ K010867/1 ). This work was also supported by the  Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET) in the United Kingdom. Harsh Gupta  acknowledges support from the  National Geophysical Research Institute, Hyderabad, India and thanks the Geological Society of India (GSI)  for use of Fig.
Reliable soil moisture retrievals from passive microwave satellite sensors are limited during certain conditions, e. g. , snow coverage, radio-frequency interference, and dense vegetation. In these cases, the retrievals can be masked using flagging algorithms. Currently available singleand multi-sensor soil moisture products utilize different flagging approaches. However, a clear overview and comparison of these approaches and their impact on soil moisture data are still lacking. For long-term climate records such as the soil moisture products of the European Space Agency (ESA) Climate Change Initiative (CCI), the effect of any flagging inconsistency resulting from combining multiple sensor datasets is not yet understood. Therefore, the first objective of this study is to review the data flagging system that is used within multi-sensor ESA CCI soil moisture products as well as the flagging systems of two other soil moisture datasets from sensors that are also used for the ESA CCI soil moisture products: The level 3 Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active/Passive (SMAP).
The SMOS and SMAP soil moisture flagging systems differ substantially in number and type of conditions considered, critical flags, and data source dependencies. The impact on the data availability of the different flagging systems were compared for the SMOS and SMAP soil moisture datasets. Major differences in data availability were observed globally, especially for northern high latitudes, mountainous regions, and equatorial latitudes (up to 37%, 33%, and 32% respectively) with large seasonal variability. These results highlight the importance of a consistent and well-performing approach that is applicable to all individual products used in long-term soil moisture data records. Consequently, the second objective of the present study is to design a consistent and model-independent flagging strategy to improve soil moisture climate records such as the ESA CCI products.
As snow cover, ice, and frozen conditions were demonstrated to have the biggest impact on data availability, a uniform satellite driven flagging strategy was designed for these conditions and evaluated against two ground observation networks. The new flagging strategy demonstrated to be a robust flagging alternative when compared to the individual flagging strategies adopted by the SMOS and SMAP soil moisture datasets with a similar performance, but with the applicability to the entire ESA CCI time record without the use of modelled approximations.
Introduction Recognized as an Essential Climate Variable in 2010, soil moisture is one of the primary drivers of water, energy, and carbon cycles  [1, 2]. Climate change-induced changes in these cycles may have a more profound effect on human and nature than global warming itself  [3]. For this reason, consistent, long-term soil moisture records are key in fully understanding the impact of climate change. Currently, long-term data records (from 1978 onwards) of soil moisture, such as the soil moisture (SM) datasets of the Climate Change Initiative (CCI) of the European Space Agency (ESA), are provided on a global scale by remote sensing satellites using passive and/or active microwave sensors  [3] [4] [5] [6] [7]. As the full global climate record is not covered by a single satellite sensor  [8] , multiple satellite sensor products have to be merged to produce a Climate Data Record (CDR).
A CDR is a time series of measurements that is characterized by a sufficient length, consistency, and continuity to capture climate variability and change. However, differences in mission design, sensor system, and retrieval algorithms have led to inconsistencies between satellite SM products  [9] [10] [11]. Over the past decade, several studies aimed to assess and improve the consistency between products [12] by conducting quality comparisons  [4, 13, 14] , sensor calibration [3,  13, 15] , uniform SM retrievals  [16, 17] , and the merging of datasets  [3, 5, 7, 16, 18]. However, to the best of our knowledge no research studies have been devoted to inconsistencies in flagging in the context of merged microwave-based soil moisture records. Although microwave observations are not dependent on daylight or cloud-free conditions as compared to optical-range observations, reliable retrievals are still limited by a range of conditions.
These conditions can be related to weather events, like snow, frost, and heavy precipitation or to the land cover in case of densely vegetated, mountainous, or deserts regions  [5]. In these conditions, retrieved SM data should be accompanied by one or more flags. A flag is commonly defined as a binary quality indicator, which aims to mark a condition in which the SM retrieval is negatively influenced. A flag can be classified either as critical or as advisory: 1. A critical flag is used by the dataset producer to decide that the retrieved SM value is not considered appropriate for dissemination and hence is replaced by Not a Number (NaN). Therefore, well-performing critical flags lead to a reduction of data availability and an improvement of the data quality; 2. An advisory flag indicates that the data value should be interpreted carefully and can be filtered out by the user.
Flagging inconsistencies in the observational conditions could easily result in the incorrect interpretation of SM values. Therefore, it is important to evaluate the differences in the current flagging strategies of key satellite SM products from dedicated SM missions that form an important input of long-term climate records such as the ESA CCI SM. In this study, we will focus on two passive SM sensors, the Soil Moisture and Ocean Salinity (SMOS) SM and the Soil Moisture Active/Passive (SMAP) SM datasets. The two single sensor datasets are chosen because of their key importance for long-term climate records (especially for low-frequency observations)  [19] [20] [21]. In addition, they are the only two input products from which auxiliary model data are extracted to flag frozen surfaces or snow in ESA CCI SM.
To give an example of the flagging differences between the two sensor datasets, SMOS SM has seven snow flags, four frozen soil flags, one frozen vegetation, and two ice flags, whereas SMAP SM has two snow/ice flags and two frozen soil flags. It would be cumbersome for the individual user to remove all flagging inconsistencies, as it would require much research to assess the data source dependencies and accuracy of all flags. A flagging system for multiple satellite SM sensor datasets already exists for the ESA CCI data products, which filters the SM values after the merging of individual sensor products. To provide insights into the flagging strategies on both the single sensor and multi-sensor level, we will provide an overview of the flagging systems of SMOS, SMAP, and ESA CCI SM. Differences in flagging systems have multiple consequences.
One of the consequences is that the spatial and temporal data availability and quality may vary between datasets resulting in differences in climatologies (and hence in anomalies). For areas characterized by a snow or a (heavy) rain season, SM data availability and quality at the beginning or end of the season are expected to differ considerably between datasets due to differences in flagging methods and threshold values. In these periods, the flag values will namely be relatively close to their thresholds. Figure  1  shows an example of insufficient flagging of snow and/or frozen soil conditions, in which the ESA CCI SM product exhibits unrealistic dry anomalies in April 2017 at the Northern Hemisphere permanent snow/ice boundaries. Another consequence is that model based flags imply a dependency of the related satellite data product.
This decreases its usefulness for independent model evaluation purposes  [5, 22]. The purpose of this paper is to present an overview of the current data flagging inconsistencies in the context of long-term ESA CCI SM datasets and to provide an alternative model-independent solution for the detected inconsistencies. The alternative flagging algorithm is designed based on the research of Grody  [23] , Zhao et al. [24] , and Jin et al. [25]  for snow, ice, and frozen soil conditions that can be applied to the individual sensor products for the entire ESA CCI SM record. Whereas the ESA CCI SM currently only uses a flag that is partially based on model data for frozen surfaces and snow, the new flagging algorithm is based on the physical relationship between the emissivity and frozen water molecules in snow, ice, and frozen soils.
Satellite Data A series of satellites with passive microwaves sensors, including the Advanced Microwave Scanning Radiometer 2 (AMSR2)  [26] , the SMOS mission  [27] , and the SMAP mission  [28]  have now been in orbit for multiple years. These sensors have been successfully monitoring SM conditions globally based on Tb measurements, which resulted in different publicly available SM products. Only SM products based on passive microwave observations were included in this study to reduce complexity. The analysis used SMOS version 300, SMAP version 6, and the ESA CCI version 4. 5 SM products and the corresponding flagging systems. Of these three, the ESA CCI SM was not included in the comparison of the flagging impact, as disentangling the impacts of filtering at the sensor level and those at the multi-sensor level was not within the scope of the paper.
The SMOS SM Sensor Data Products The first satellite that was dedicated and designed for measuring surface SM and sea surface salinity was the SMOS satellite launched in 2009 by the European Space Agency (ESA). The satellite boards a polar-orbiting 2-D interferometric radiometer at L-band (1. 4 GHz)  [4, 27, 37]. This frequency is not only chosen because it is the most favorable frequency for retrieving SM  [38]  and sea surface salinity, it is also the only protected frequency below 5 GHz  [39, 40]. SMOS follows a sun-synchronous orbit with local time 6 A. M. (ascending)/P. M. (descending) with a revisit time between 1 and 3 days  [41]. The swath width of SMOS is 1000 km and the nominal resolution is 25 km to 50 km depending on the position of the footprint within the field of view  [42].
Level 3 (L3) ascending SM data from the Centre Aval de Traitement de Données SMOS (CATDS) provided in a 25 km Equal Area Scalable Earth (EASE) grid Version 2 were used for this study  [41, 43, 44]. The global L3 SM is composed of multi-orbit retrievals. Each retrieval derives SM from the multi-angular (0-55 degree) L-band Tb measurements for two orthogonal polarizations  [45].
The SMAP SM Sensor Data Products The SMAP mission, launched by the National Aeronautics and Space Administration (NASA) in 2015  [26] , was designed to globally monitor the SM of the Earth's surface. The SMAP satellite includes both a passive radiometer and an active radar at L-band that measure the status of the hydrosphere in a syn-synchronous orbit with local time 6 A. M. (descending)/P. M (ascending) consisting of a 1000 km swath, with a resolution of about 40 km, and a revisit time of 2-3 days  [46]. Due to an irrecoverable hardware failure of the radar, only the radiometer-derived SM product became operational, using the observed Tb  [47]. In this study, the Global Daily 36 km EASE-grid passive radiometer Level 3 SM product (L3_SM_P v6, accessible via https://nsidc. org/data/SPL3SMP/versions/6) was used for the period 01 January 2016 to 31 December 2019.
The ESA CCI SM Multi-Sensor Data Products Within ESA CCI, a set of active and passive Level 2 (i. e. , in swath geometry) SM datasets are merged into a harmonized global record based on a thorough understanding of their error characteristics (http://www. esa-soilmoisture-cci. org) [3, 5, 7, 18, 48]. To obtain consistency in methodology across the different sensors, only active microwave products from ESA and H-SAF, derived with the TU Wien method [49] and passive microwave products retrieved with the Land Parameter Retrieval Model (LPRM)  [16]  are currently being used in ESA CCI SM products  [5]. In the present study, only the flags of the passive microwave SM product (ESA CCI SM v04. 5, http://dx. doi. org/10. 5285/38b8e5e524e1449ab4b4994970752644) were considered. For more information on ESA CCI SM products please refer to Dorigo et al.
From both networks, the data of soil temperature (TS) measured at 6 am of the top layer (the highest within the first 8 cm) and snow water equivalent (SWE) were analyzed in this study. The precision of SNOTEL and SCAN observations is the same for both SWE measurements (0. 1 inches; 2. 54 mm) and TS measurements 0. 1 • C  [52]. All stations from the SNOTEL and SCAN networks that measured these two variables for the period from 2016 to 2019 were included, which are 390 and nine stations respectively of which the locations are depicted in Figure  2.
ESA CCI SM Flags The ESA CCI SM dataset has a flagging system in place that can be applied on the Tb level of all input products. Directly based on LPRM, the flags mask SM products in the case of dense vegetation, snow cover, temperature below 0 • C, and failure of the SM retrieval  [53]. In the case that multiple input products are not available at a given time and location (due to, for example, flagging), it is considered whether the remaining dataset(s) still have valid observations that do not exceed the maximum error variance threshold  [5]. This flagging approach might result in less days that are flagged for snow cover and frozen conditions in the beginning and at the end of a winter season. Figure  3  visualizes the "Snow_coverage_or_temperature_below_zero" flag of ESA CCI SM, that is solely based on a threshold for the land surface temperature.
SMOS SM Flags The flags in the SMOS SM products consist of four categories: Product science flags, event flags, S_Tree_1 flags, and retrieval flags. The event flags and the S_Tree_1 flags are provided to the users within the SM data products  [45]. Event detection is for flagging events such as freezing, dew, snow, ice, or flood. Currently, only freezing events of soil and/or vegetation are implemented  [44]. However, regions in which the snow layer is thick enough to affect L-band Tb are often excluded from SM retrieval based on masks for highly variable topography or permanent snow cover  [44]. The detection of soil freezing is based on both the auxiliary IFS HRES ECMWF forecast data (i. e. , the temperature of the first soil layer) and the actual status and temporal evolution of the retrieved parameters (e.
Including a limit on the Tb18. 7V for the detection of snow conditions was already implemented in Foster et al. [63]  and expected to result in a more homogenous flagging for Greenland, as this frequency is less sensitive to variations in snow geophysical properties (i. e. , density, depth, and grain size)  [64, 65]. Spatial analysis for all seasons showed the best results of a homogenous flagging over Greenland with the third threshold (i. e. , <245). Here we assume that snow/frozen conditions do not vary that much within the time difference of overpass (order of ~1 day), so that a L-band only sensor dataset can be flagged by applying the flag of another sensor dataset for that day. Note that small inconsistencies could still be present between different sensors and frequencies related to differences in footprint sizes, swath widths, and overpass times.
Flagging Differences The influence of data flagging differences on data availability was analyzed globally for the period 2016-2019. This enabled the detection of where and when the flagging differences have the most impact on any climate study that is based on the SMOS, SMAP, or ESA CCI SM datasets. To represent the impact of flags on data availability, we defined the total flagging intensity that is the average number of NaNs in the SM record. The flagging intensity of snow, ice, or frozen conditions was calculated by deriving a combined critical snow, frozen soil, and ice (SFI) flag for each of the two single sensor data products and averaging the number of times the combined flag operates as a mask for SM observations. The SFI flag of the new method was computed by counting the number of times the classification of snow or frozen flag was reached for AMSR2 following the decision tree in Figure  4.
The true positives (TP) and true negatives (TN) reflect the exact opposite situations, indicating correct representation of a condition that should be flagged or not flagged by the SFI flag value compared to the reference data. The false discovery rate (FDR), the false omission rate (FOR), and the accuracy (ACC) were calculated according to: FDR = FP (FP + TP) (1) FOR = FN (FN + TN) (2) ACC = (TP + TN) Total population (3) By considering the final performance results, we found that t1 and t2 are representative for the type of snow/frozen conditions captured by the flagging systems of all three datasets. The shift in FDR is namely opposite to the shift in FOR when comparing threshold t1 to threshold t2 for all three datasets, which shows that the optimal threshold is found somewhere in between t1 and t2 of which the exact position differs per SFI flag.
Differences in Flagging Systems The flagging systems at the single sensor level for SMOS, SMAP, and at the multi-sensor level for ESA CCI SM were compared in the number of flags and types of data source used for these flags. Seven flagging categories were defined based on mutual occurrence: "Snow/frozen", "Precipitation", "Open water", "RFI", "Urban areas", "Vegetation", and "Topography". The "Other" category includes all residual flags, among which coastal flags, wetlands flags, and flags related to unrealistic SM results. For a summary overview of the flagging differences, please see Table  2  (for more details please see Tables A1-A3 in Appendix A). Figure  5  demonstrates the diversity in the number of flags per category and in total. The single sensor SM datasets SMOS and SMAP both have a high complexity in flagging with approximately seven to eight times as many flags as the ESA CCI SM.
The category with the highest number of flags considering all datasets is the "Snow/frozen" category, with 11 out of the 31 critical flags (i. e. , ~36%). SMOS SM has more flags in the "Snow/frozen", "Open water", "Urban areas", and "Topography" categories, than SMAP and ESA CCI SM. In contrast, SMAP SM has the most flags in the "Other" category, dealing with many quality aspects. However, the number of critical flags is highest for SMOS SM (e. g. , 15). Every flag within ESA CCI SM except one is critical and for none of the seven categories it has multiple flags. Although not within the scope of this paper, it would be relevant to review the quality of the different flagging systems for all conditions. Then we would be able to assess the balance between more complex, but likely more accurate flagging systems (such as those implemented for SMOS and SMAP SM) and more simple, but likely less accurate flagging systems (such as the one for ESA CCI SM).
It is also the only one within the ESA CCI SM flagging system that is partially model-dependent. Snow and frozen conditions are separately flagged by both SMOS SM and SMAP SM, so one could argue to use two flagging categories. However, the complexity in the number of snow/frozen/ice conditions varies considerably between the three datasets, ranging from 15 flags for SMOS SM to one for the ESA CCI SM. Besides, the flagging strategy proposed here masks both the influence of frozen soil water and of snow particles on the microwave signal. For these reasons we decided to take the snow and frozen conditions as one category. The frozen ground condition of SMAP SM is based on both model results and on its own official Freeze/Thaw State product  [28]. The latter is based directly on the Tb's but is only advisory.