Lakes and wetlands are natural sources and have an important role for hydrological cycle. They are also affected by climate change and anthropogenic activities such as precipitation regime, diminishing water bodies, uncontrolled groundwater extraction, drainage and irrigation channels, agricultural activities and discharge of domestic and industrial waste. For this reasons monitoring the lake surface areas are very crucial. In this paper; the Seyfe Lake was selected as a case study. The study area is located at the southern part of the Seyfe basin with a catchment area of 1447 km2 in Central Anatolia. Seyfe Lake wetland is located in a closed basin. The lake area and its surroundings is a Ramsar site and have the status of Grade 1 Natural Site and Protected Natural Area. This study aimed to evaluate the spatio-temporal changes of the surface area of Seyfe Lake by using Geographical Information Systems (GIS) and remote sensing techniques. For this purpose Landsat (MSS, TM, ETM+, OLİ/TIRS) satellite images used for a period between 1987 and 2020. A total of 34 images were processed and, using unsupervised classification method, water body was extracted according to the spectral information of the image bands and the historical change of the lake surface area was calculated by using Image Analysis Function of ArcGIS 10.6.1 software. In addition to these satellite images, long-term hydrometeorological data and field observations were analyzed to determine the changes of the Seyfe Lake surface area and understand the effects of climate change and antropojenic activities. According to the old and recent satellite images of Seyfe Lake area, the persisting shrinkage of lake area and diminishing of the water resources over the years in the Seyfe Lake basin still continue. Maximum and minumum surface area of the lake was calculated 43 km2 and 18 km2 respectively during 1987-2020. In August 2008; the lake area is completely dried. Nowadays the 58% of the lake area is dried up. These variations could probably be related with the antropogenic effects and climatic changes in the study area. As a result, Satellite based techniques might provide a historical data to identify changes on the lake surface area. Remote sensing techniques are also effective to evaluate temporal and spatial changes on water resources resulted by human activities and local climate change. In-situ observation and hydrogeological monitoring are another important tools to support findings of remote sensing study. Keywords: Spatio-temporal change, Wetland, Seyfe Lake, Unsupervised Classification, Remote Sensing

The Salton basin is an arid, sub-sea level basin located in southeastern California. Mountain ranges border the basin directly to the west and east, to the north extends a narrow valley that terminates with a transverse mountain range, and to the south spans heavily irrigated, agricultural lands that gradually rise in surface elevation. Within the basin lies the Salton Sea, and surrounding the Sea is diverse terrain that includes dry playa, rocky and vegetated surfaces, and highly emissive alluvial fans and dry washes, among others. Although large dust outbreaks due to orographically forced high wind speed events are frequent in the area, measurements from a new AERONET site stationed within the basin shows that dust is a standard component of the region's atmosphere, even on days with otherwise low wind speeds. We find a diurnal cycle of background dustiness that peaks in the early afternoon. An analysis of boundary layer structure using a Vaisala CL51 suggests that deepening of the boundary layer due to dry convection results in downward mixing of momentum, increased surface wind speeds, and an accumulation of dust in the mixed layer. Further analysis of the CL51 observations at nighttime suggest that high AOD concentrations persist in the shallow nocturnal boundary layer, and can become elevated throughout the night due to westerly katabatic flows. An analysis of surface meteorological station data suggests possible pathways of suspended dust due to mountain and valley flows within the basin.

How and why has Geographic Information Systems (GIS) education been advancing in primary and secondary education, globally? How have GIS tools and approaches been spreading beyond geography and GIScience to earth and environmental science, business, data science, health, and other disciplines in higher education? The adoption and integration of GIS and related geospatial technologies into dozens of academic disciplines has led to worldwide high demand for instruction that is targeted and timely, a combination that is challenging to meet consistently with diverse audiences and in diverse settings. Over the past 40 years, methods and approaches for teaching and learning about geospatial technologies and with geospatial technologies have evolved in tight connection with the advances in the internet and personal computers. Academic degrees, concentrations, minors, certificates, and other programs abound within formal and informal education. During the educationally-disruptive COVID-19 period, online instruction has hastened the adoption of analysis-based and data-rich approaches such as GIS. What are the challenges that remain in the use of GIS in instruction and research, and how can these challenges be addressed? The author, who has been active in promoting and researching GIS in education at all levels for 30 years, will share his perspectives, research results, and offer a pathway forward.

The CYGNSS constellation of eight satellites was successfully launched in December 2016 into a low inclination (tropical) Earth orbit. Each satellite carries a four-channel bistatic radar receiver which measures signals transmitted by Global Positioning System (GPS) satellites and scattered back into space by the Earth surface. Over the ocean, surface roughness, near-surface wind speed and air-sea latent heat flux are estimated from the surface scattering cross section. Over the land, estimates of near-surface soil moisture and imaging of inland water bodies and flood inundation are derived from the surface reflectivity. The measurements are able to penetrate through all levels of precipitation and through most vegetation canopies due to the long radio wavelength at which GPS operates. The number of satellites in the constellation and their continuous data-taking operation produces high spatial sampling density and low temporal revisit times. Over ocean, this makes possible the reliable detection of tropical cyclone intensification and the resolution of diurnal cycles in tropical winds. Over land, diurnal soil moisture variability is resolved and rapidly changing flood inundation events are mapped. Engineering commissioning of the constellation was completed in March 2017 and the mission is currently in its science operations phase. Science data products are regularly produced over ocean for wind speed, surface roughness, and sensible and latent heat fluxes and over land for near surface volumetric soil moisture. Data products currently in development over ocean include tropical cyclone intensity (peak sustained winds) size (radius of maximum winds), extent (34, 50 and 64 knot wind radii), storm center location, and integrated kinetic energy. Over land, data products in development include refined versions of volumetric soil moisture content, flood inundation extent, time-varying inland water body maps, and riverine streamflow rate. An overview and the current status of the CYGNSS mission will be presented, together with updates on terrestrial science data products in development that are related to the terrestrial water cycle.

The Surface Biology and Geology global imaging spectrometer is primarily designed to observe the chemical fingerprint of the Earth’s surface. However imaging spectroscopy across the visible to shortwave infrared (VSWIR) can also provide important atmospheric observations of methane point sources, highly concentrated emissions from energy, waste management and livestock operations. Relating these point-source observations to greenhouse gas inventories and coarser, regional methane observations from sensors like the European Space Agency (ESA) TROPOMI will contribute to reducing uncertainties in local, regional and global carbon budgets. We present the Multi-scale Methane Analytic Framework (M2AF) that facilitates disentangling confounding processes by streamlining analysis of cross-scale, multi-sensor methane observations across three key, overlapping spatial scales: 1) global to regional scale, 2) regional to local scale, and 3) facility (point source scale). M2AF is an information system that bridges methane research and applied science by integrating tiered observations of methane from surface measurements, airborne sensors and satellite. Reducing uncertainty in methane fluxes with multi-scale analyses can improve carbon accounting and attribution which is valuable to both formulation and verification of mitigation actions. M2AF lays the foundation for extending existing methane analysis systems beyond their current experimental states, reducing latency and cost of methane data analysis and improving accessibility by researchers and decision makers. M2AF leverages the NASA Methane Source Finder (MSF), the NASA Science Data Analytics Platform (SDAP), Amazon Web Services (AWS) and two supercomputers for fast, on-demand analytics of cross-scale, integrated, quality-controlled methane flux estimates.

Landcover information is of relevance for a range of applications in Arctic environments including up-scaling of in situ measurements, permafrost monitoring and modelling and climate change impact assessment. But not only natural landcover types, also the identification of human impacted areas is required. A wide range of satellite records are available, but often there are coverage and resolution limitations. Due to the spatial heterogeneity typical for the Arctic, an as high as possible spatial resolution is needed. Sentinel-1 as well as Sentinel-2 offer 10m nominal resolution for specific bands and modes and are freely available across the entire Arctic. The multi-spectral information from Sentinel-2 is specifically of value for discrimination of tundra types. The Synthetic Aperture Radar mission Sentinel-1 provides added value through representation of land surface structure features. A combination of both allows significantly improved characterization of landcover over larger areas. Here, we review recent progress in monitoring the land surface close to Arctic coasts (focus region of HORIZON2020 Nunataryuk), specifically the distribution of human impacted areas beyond of what is represented in databases such as OpenStreetMap or global settlement maps. The added value of machine learning techniques will be discussed and results based on Sentinel-1 und Sentinel-2 presented. Further on, permafrost change information provided through the ESA CCI+ Permafrost project is combined with the novel maps. The CCI+ Permafrost datasets cover 1997-2018 for ground temperature, active layer thickness and permafrost fraction with 1 km gridding (partially derived from MODIS LST, https://climate.esa.int/en/projects/permafrost/data/) and therefore allow detailed assessment of areas with human presence over permafrost.

Electric field measurements inside thunderstorms are essential to our understanding of thunderstorm charge structure, electrification, and lightning initiation. However, most existing measurements have been made by single instruments carried aloft by weather balloon, thus providing measurements made at a single point that moves through the storm on a timescale of tens of minutes. It is therefore difficult to interpret such data, since a change in observed field strength may be due to motion of the balloon into a region with different field or due to overall evolution of the storm's electrical structure with time. Separation of such spatial and temporal variability requires simultaneous measurements at multiple locations within the storm. This can be accomplished with a single weather balloon by carrying multiple independent electric field dropsondes aloft and releasing them one at a time, separated by short time intervals. The balloon payload design is optimized for low mass and use of off-the-shelf components whenever possible, releasing each dropsonde by a hot wire cut-down mechanism. Each dropsonde spins as it falls, measuring electric field as it rotates and sends data to a ground station in real-time. The dropsondes are designed to fall and rotate stably by use of aerodynamic simulations, with internal components robustly connected along the axis of the instrument to ensure the desired balance and alignment of the principal axes of the moment of inertia. The telemetry transmitters use simple low-cost low-power all-in-one transmitter chips. The telemetry ground station receives signals simultaneously from all dropsondes by a single software-defined radio receiver. Robust long-range communication is enabled by use of spread spectrum techniques and error correcting codes.

We examine long-term energetic heavy ion measurements including three planets’ magnetospheres, focusing on Fe ions (specifically, but not exclusively, Fe+) in and near Earth’s magnetosphere. We compare Fe data to that of other energetic ion species with masses greater than C (carbon) and consider the relationship(s) of energetic Fe ion measurements at the three planets to internal (ionospheres, exospheres, moons, rings, and trapped radiation) and external (solar wind and interplanetary dust) source candidates. Fe+ has been observed at Earth and Saturn, but not yet at Jupiter, as our observations there were brief. The measurements are from two functionally identical charge-energy-mass ion spectrometers: one on Geotail (~87-212 keV/e), orbiting Earth at ~9-30 Re; and the other on Cassini (~83-167 keV/e), in interplanetary space, during Jupiter flyby, and at ~4-20 Rs on its constantly varying orbits around Saturn. These ion spectrometers efficiently separate energetic light and heavy ions by mass, as well as lower charge state ions from higher charge state ions by mass-per-charge. Energetic low charge state ions often derive from magnetospheric sources, while energetic high charge state ions most often derive from the solar wind. We also enlist heavy ion measurements closer to the Earth from AMPTE/CCE which are used for C and Fe radiation-belt- modeling content, consideration, and estimation.

Recent observations and simulations have revealed a wide variety of plasma processes and multiscale structures at the dayside magnetopause. In this presentation, we focus on the origins and evolution of the plasma populations observed in the magnetopause boundary layers. We present the results of Particle-In-Cell (PIC) simulations encompassing large volumes of the dayside magnetosphere. The implicit PIC code used in the study was initialized from a global MHD state of the magnetosphere for southward interplanetary field conditions. Three-dimensional plots of the perpendicular slippage indicates that reconnection occurs over most of the dayside magnetopause. However, the simulation reveals that the reconnection region has a much more filamentary structure than the X-line expected from the extrapolation of 2D models and that multiscale structures thread the reconnection outflow. In particular, the simulation indicates the formation of multiple layers of electrons with significant field-aligned velocities within the main magnetopause current layer. We use velocity distribution functions at different locations in the reconnection outflow to characterize the origins and evolution of the electron and ion populations of the magnetosheath and magnetospheric boundary layers.

In-situ novelty-based target selection of scientifically interesting (“novel”) surface features can expedite follow-up observations and new discoveries for the Mars Science Laboratory (MSL) rover and other planetary exploration missions. This study aims to identify which methods perform best for detecting novel surface features in MSL Navcam images for follow-up observations with the ChemCam instrument, as a complement to the existing Autonomous Exploration for Gathering Increased Science (AEGIS) onboard targeting system. We created a dataset of 6630 candidate targets within Navcam grayscale images acquired between sols 1343-2578 using the Rockster algorithm. These were the same target candidates considered by AEGIS, chosen to enable direct comparison to past AEGIS target selections. We employed five novelty detection methods, namely Discovery via Eigenbasis Modeling of Uninteresting Data (DEMUD), Isolation Forest, Principal Component Analysis (PCA), Reed-Xiaoli (RX) detector, and Local RX. To evaluate the algorithm selections, a member of the MSL science operations team independently identified candidate targets that represented example scenarios of novel geology that we would desire an algorithm to identify, such as layered rocks, light-toned unusual textures, and small light-toned veins. We compared these methods to selections made by AEGIS and a random baseline. Initial experiments for three scenarios showed that Local RX most frequently prioritized novel targets, followed by DEMUD and AEGIS. Our next steps in this study include evaluating input feature representations other than pixel intensities (e.g., Histogram of Oriented Gradients features), performing additional experiments to evaluate novel target prioritization performance, and selecting target candidates in Mastcam color images.

For catchment-scale streamflow estimation, recently, the preference is shifting towards the use of innovative remote sensing (RS)-based approaches at the remote gauging stations. In this context, this study evaluates the performances of three RS-based models designed with the near-infrared (NIR) bands of Landsat images at the Jenapur outlet of the Brahmani River basin in eastern India. These RS-based models are designed using the spectral behavior of land (C) and water (M) pixels in the NIR region of the electromagnetic spectrum in the presence and absence of water surrounding the streamflow gauging station. Further, the computed pixel ratio (C/M) is used as a parameter for the discharge estimation, in which four years (2009-2013) of Landsat images are used during calibration and three years (2014-2016) of these images are used during validation. Model-I uses the C/M method in which a box-matrix is conceptualized to analyze the optimal location of the land pixel (C0); and subsequently, the time series of C/M is calibrated with the in-situ discharge (Q) time series. The best pixel ratio (C0/M) time series is preprocessed with an exponential smoothing filter to derive the best filtered-pixel ratio (C0/M*) time series, which is used in the regression model to estimate the river discharge. Model-II corresponds to the multi-pixel ratio (MPR) method, where a 3×3 window is used to calculate the average reflectance of both the C and M pixels within the box-matrix, and subsequently, to obtain the best pixel (C0ʹ/Mʹ) ratio as in the case of Model-I to develop the spectral relationship between C0ʹ/Mʹ and Q time series. Model-III uses both the C/M and water width-based function to estimate the streamflow. The performance evaluation of the models is carried out using the Nash-Sutcliffe efficiency, Percentage bias, and Mean absolute error, which reveals that the model performance varies in the order: Model-III > Model-II > Model-I. This proposed RS-based discharge estimation model framework has the potential to be used in many world- rivers with varying cross-sections.

Urban Green Spaces (UGSs) are proving to be most important part of urban area of a city. These green spaces not only provide psychological comfort to humans but also affect heat impact on city to a vast level. In a developing country like India, where urban growth is happening in a very fast and haphazard manner a little consideration is given to Green Spaces in city. Such a study has been conducted over Lucknow metro-city of Uttar Pradesh state of India. In this study, the relation between Land Use\Land Cover (LULC) and Land surface temperature (LST) has been tried to find. Using Landsat-8 OLI data (Band 3, 4 & 5) and Maximum Likelihood Classification algorithm, 6 Land Use classes are obtained, which are “Built-up”, “Vegetation”, “Shrub”, “Water”, “Fallow Land” and “Other”. In addition to these 4 Land Cover Indices namely Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Difference Barren Index (NDBaI) are also generated for same areas. LST is obtained using TIRS data (Band 10 & 11) and Split Window Algorithm by Radiative Transfer Equation. Ancillary data is used for digitization of 5 assembly constituencies (ACs) of Lucknow parliamentary constituency (PC) of Lucknow district namely, Lucknow Cantonment, Lucknow North, Lucknow East, Lucknow West and Lucknow Central has been done. River and Canal passing through these areas, are also considered in Urban Green Spaces as per Urban Atlas code 14100. 250m radius buffer is generated around Built-up pixels for analysis of impact of UGSs on LST. Cantonment and East Lucknow area having highest amount of UGSs in terms of “Vegetation”, “Shrub” & “Water” pixels due to presence of Forest area in both ACs. It is found that LST is positively related with all indices except for NDVI with strong negative correlation and R2 of 0.47 and highest R2 of 0.53 with NDBI. Among all 5 ACs best correlation between all 4 LC and LST values is found in Lucknow East AC with R2 > 0.64 for NDVI, NDWI and NDBI. Lucknow East AC is having least LST but there is very little difference between LST values of Built-up pixels having minimum UGSs present in 250m radius buffer around built-ups and Built-up pixels having no UGSs around. AC Lucknow Central is having 1 °C difference in LST values of such different Built-ups.

Multicollector noble gas mass spectrometry has yielded significant improvements in the precision of 40Ar/39Ar age determinations over the last decade [1,2]. We report initial results from the new Isotopx NGX mass spectometer at the WiscAr Laboratory that is equipped with 9 Faraday detectors and 1 ion counting multiplier. The NGX utilizes Isotopx’s ATONA® amplifier technology, enabling measurements spanning a dynamic range from below 10−16 A to above 10−9 A [3]. Moreover, the WiscAr NGX offers the unique opportunity to evaluate instrument performance using both a conventional, and a newer low temperature, Nier-type ion source, which allows for trap current variation. We have performed a series of tests to: (1) assess optimal measurement and integration times for blanks, baselines, and air aliquots of various sizes, (2) quantify the NGX sensitivity via measurements of first principle 40Ar/39Ar standards fused with a CO2 laser, and (3) compare the sensitivity and effects on instrument background between the conventional and new low temperature Nier-type sources. Initial results suggest that for most samples optimal precision is achieved when using 10 second integration times for measurements lasting 400-600 seconds. Measurements of low 36Ar signals are optimized with longer integration and measurement times. In addition to measurement optimization experiments, we performed a comparative analysis of both fusion and incremental heating data obtained using the WiscAr Nu Instruments Noblesse, MAP 215-50, and the NGX for a variety of geologic materials which range in age from Pleistocene to Permian. Pychron software [4] controls the entire NGX analytical system, including data collection and reduction. Careful experiment design in this automated system, when informed by optimized counting practices and knowledge of collector sensitivity, can lead to high precision 40Ar/39Ar dates. [1] Jicha et al. (2016), Chemical Geology 431, 54–66 [2] Mark et al. (2009), Geochemistry, Geophysics, Geosystems 10 [3] Cox et al. (2020), Geochron. Discuss, 2020-1. [4] Ross J., 2019, NMGRL/pychron v18.2, doi: 10.5281/zenodo.3237834.

Large igneous provinces (LIPs) are often associated with mass extinctions and are thus vital for life evolution on Earth. However, the precise relation between LIPs and their impacts on biodiversity is enigmatic as they can be asynchronous. If the environmental impacts are primarily related to sill emplacement, the structure of LIPs’ magma storage system becomes critical as it dictates the occurrence and timing of mass extinction. Here we use surface wave tomography to image the lithosphere under the Permian Emeishan Large Igneous Province (ELIP) in southwestern China. We find a NE-trending zone of high shear-wave velocity (Vs) and negative radial anisotropy (Vsv > Vsh) in the crust and lithosphere and interpret it as a mafic-ultramafic, dike-dominated magma storage system on the hidden hotspot track of the ELIP. An area of less-negative radial anisotropy, on the hotspot track but away from the eruption center, reflects an elevated proportion of sills emplaced at the incipient stage of the ELIP. Liberation of poisonous gases and mercury by the sills explains why the mid-Capitanian global biota crisis preceded the peak ELIP eruption by 2-3 million years.

Satellite imagery to rapidly develop maps of historical flood hazard and current inundated areas over large spatial coverage is indispensable in supporting situational awareness for improved debris estimation, transportation impacts and damage assessments. However, how best to utilize these maps as actionable information during flood disasters and for flood disaster response assistance is less clear. Furthermore, the integration of any satellite data from an “untrusted” (non-mandated) source into the operations chain and response protocols of a mandated agencies such as FEMA, PDC (PDC is already pulling some DFO-DSS layers) or the UN WFP would be a non-trivial procedure. These agencies desire to prioritize support and resource requirements for community lifelines. (Safety & Security; Food, Water & Shelter; Health & Medical; Energy (Power & Fuel); Communications; Transportation; and Hazardous Materials). The majority of these lifelines can be impacted by floods. The Global Flood Observatory’s (DFO, University of Colorado Boulder) web map server and its associated mobile app (DFO-Floods) is a resource for global extents of floods now delivered as map products via web services. This flood decision support system (DSS) serves flood maps along with other trusted geospatial data to the global disaster response community. However, acceptance of the DFO product line as a trusted information source requires additional tests to assess its performance in combination with the respective response process of agencies around the world. This would allow moving the product from a high Application Readiness Level into an Operational Readiness Level (ORL) for agency trusted data implementation. This paper reviews success examples of the DFO flood layers, illustrates the newly released mobile app and discusses the need for trusted flood map products and services to support the global disaster response community.

C-LIFE is a landed camera suite, suitable for Ocean World surfaces, consisting of a color Context Reconnaissance Stereo Imager and an LED flashlight that can also identify biogenic material through fluorescence. The C-LIFE design leverages ongoing work (with industry partners Space Dynamics Lab, SRI International and Ball Aerospace) from our ICEE-2 and COLDTech awards in low-temperature detector qualification, radiation modeling and mechanical design. It takes advantage of our development work on the Descent Imager for Europa Hazard Avoidance and Radiation Durability (DIEHARD) and camera development on other missions. The C-LIFE camera head is mounted on the Europa Lander’s high gain antenna, which provides tilt and pan capability. Minimal electronics in the camera head control and read out the detector and LEDs. Electronics in the lander vault perform most camera functions and image processing. C-LIFE contains no moving parts. In lieu of a focus mechanism, C-LIFE utilizes high F/number optics and a field of view elongated in the vertical direction with progressive focus from top (infinity e.g. imaging horizon) to bottom (close e.g. imaging sample delivery port). Strip filters (that match Europa Clipper’s Europa Imaging System) on the detectors and partly-overlapping images provide color coverage. Heaters warm C-LIFE to operating temperatures, allowing self-heating to take over, but otherwise the camera is unheated in order to conserve energy. We combine two independent eyes into one mechanical housing (Figure 1) with a dual periscope design, which reduces the mechanical envelope, shielding mass, heating energy and total cabling distance. We use LEDs in three bands to illuminate and to excite fluorescence. Fluorescence excited by these three bands can identify the presence of key metabolic biomarkers and discriminate the quantities of live cells, dead cells and spores in a terrestrial setting. Figure 1. A dual-periscope design, with eyes 20 cm apart, folds optical trains (incoming rays in gray) to mutually shield focal planes. Fold mirrors locate LEDs inside the camera for shielding.

We study the behavior of solar wind (SW) parameters over 21-24 solar cycles (SCs). We take into account that these variation can depend on three reasons: (1) type of SW, (2) phase of SC, and (3) SC number. Our catalog of large-scale SW types for 1976-2018 (see the website ftp://ftp.iki.rssi.ru/pub/omni/ [Yermolaev et al., 2009]) covers the period from the 21st to the 24th SC and allows us to analyze independently these three reasons, since we can select SW parameters for different types of SW (in particularly, slow and fast SW, Sheath, ICME, CIR) and at different phases of different SCs. Preliminary data show that with an increase in the SC number (1) the number of perturbed SW types decreases and (2) the values of plasma and magnetic field parameters decrease in each type of SW, and for each phases of SCs. The most rapid change in parameters was observed during the minimum period between the 22nd and 23rd cycles. The work was supported by the Russian Science Foundation, grant 16-12-10062. References Yermolaev, Y.I., Nikolaeva, N.S., Lodkina, I.G., Yermolaev, M. Y. (2009). Catalog of large-scale solar wind phenomena during 1976–2000. Cosmic Res 47, 81–94 https://doi.org/10.1134/S0010952509020014Yermolaev, Y.I., Nikolaeva, N.S., Lodkina, I.G., Yermolaev, M. Y. (2015). Dynamics of large-scale solar wind streams obtained by the double superposed epoch analysis, JGR, https://doi.org/10.1002/2015JA021274

Monitoring stress state in the Earth’s crust plays a crucial role in our understanding of an earthquake’s mechanism, especially how earthquake ruptures nucleate, as well as in calculating the distribution of hazards. Crustal deformation due to the 2019 Ridgecrest earthquakes, which occurred near the town of Ridgecrest, California, that culminated in a preceding earthquake of magnitude (M) 6.4 and a subsequent M7.1 event, caused stress perturbation in nearby regions. However, implications of future seismic activity are still unclear. Here we analyze the occurrence of small earthquakes compared to larger ones—the b-values, showing how the nucleation area for both the M6.4 and M7.1 earthquakes had low b-values before these events occurred, and mid-to-high b-values thereafter. The slip distribution of the M7.1 event is also well correlated with the b-value map. Additionally, the time and local-dependent variations in b-values of the Ridgecrest earthquakes are linked with estimates of changes to Coulomb stress. The main conclusion is that the b-value mapping provide insight into the stress state in the fault zone, which is likely closely related to the nucleation and evolution of earthquakes in the sequence. The combined approach of stress-change and b-value analyses to the post-M7.1-quake sequence shows an area that is currently being stressed near the Garlock fault that hosted past large earthquakes. The b-values are not as low as those immediately before the M6.4 and M7.1 events, but contribute the most recent values in a decreasing trend of the b-value. Together with geodetic and seismological observations, monitoring the spatial and temporal distribution of b-value would contribute to seismic hazards in the Eastern California Shear Zone.