Earth Observing System Research Articles

On-orbit Moving Target Detection Method Based on Dual Images from Taijing-IV 01 Satellite

Abstract. With the evolution of satellite video technology, the domain has garnered increasing attention. Concurrently, advancements in deep learning have yielded numerous outcomes in target detection. This paper introduces a novel method for detecting moving targets, offering a broader detection range compared to traditional satellite video techniques, facilitating orbital target recognition from dual panchromatic image strips. Our experimental setup on the Taijing-IV 01 satellite, launched on February 27, 2022, successfully acquired two image strips separated by one second. These strips contain speed and directional information of moving objects, extractable through the frame differencing technique. We propose combining frame differencing with lightweight deep learning for target detection, extracting regions of interest (ROIs) to focus on areas with potential moving targets. This approach reduces the workload of wholeimage target detection, decreasing data processing volume by 89%. By optimizing the YOLOv8 network and using techniques like feature map fusion of low-level and high-resolution features, we enhance sensitivity to small targets. Consequently, the model size is reduced by 79%, the mean Average Precision (mAP) increases by approximately 1.8% and 4.5%, and detection speed rises by 26%. This method introduces a new paradigm in remote sensing data services, facilitating rapid acquisition and real-time transmission of positions and image information of moving targets to the ground. This significantly reduces bandwidth requirements for transmitting remote sensing information, presenting a novel strategy for data acquisition and processing in large-scale Earth observation systems and geoscientific applications.

Blue-cloud DAB: developing a platform to harmonize, assess and disseminate marine metadata collections

AbstractThe integration and harmonization of marine data from diverse sources are vital for advancing global oceanographic research and ensuring seamless discovery and access of critical datasets. This paper presents a comprehensive analysis of the metadata harmonization efforts within the Blue-cloud 2026 project, which brokers data from numerous Blue Data Infrastructures (BDIs), leveraging the Discovery and Access Broker technology. The platform enables discovery and analysis of marine data collections while facilitating interoperability with other components of the marine digital ecosystem, such as virtual laboratories and the Semantic Analyzer. It also supports the flow of Blue-cloud information to other initiatives like the Global Earth Observations System of Systems. For data managers, the findings emphasize the importance of enhancing metadata quality, revealing discrepancies in core metadata elements, and the need for more consistent use of controlled vocabularies. For cyberinfrastructure developers, the study details the challenges of accommodating a wide array of interfaces from different data systems, highlighting the adoption of an extensible brokering architecture that harmonizes metadata models and protocols. The study also emphasizes the importance of metadata analysis in ensuring effective searches for end users, highlighting challenges in aggregating diverse sources, where data providers may have structured the content with different objectives compared to those of the system of systems. End users will gain insights into the current metadata content of Blue-cloud, enabling them to search and access data from multiple BDIs with an understanding of the technical complexities behind the scenes.

Intersensor Calibration of Spaceborne Passive Microwave Radiometers and Algorithm Tuning for Long-Term Sea Ice Trend Analysis Based on AMSR-E Observations

Sea ice monitoring is key to analyzing the Earth’s climate system. Long-term sea ice extent (SIE) has been continuously monitored using various spaceborne passive microwave radiometers (PMRs) since November 1978. As the lifetime of a satellite is usually approximately 5 years, bias caused by differences in PMRs should be eliminated to obtain objective SIE trends. Most sea ice products have been analyzed for long-term trends with a bias adjustment based on the coarse resolution special sensor microwave imager (SSM/I) in operation for the longest period. However, since 2002, Japanese microwave radiometers of the Advanced Microwave Scanning Radiometer (AMSR) series, which have the highest spatial resolution in PMR, have been available. In this study, we developed standardization techniques for processing SIE including calibration of the brightness temperature (TB), tuning the sea ice concentration (SIC) algorithm, and adjusting the SIC threshold to retrieve a consistent SIE trend based on the AMSR for the Earth Observing System (AMSR-E, one of the AMSR that operated from May 2002 to October 2011). Analysis results showed that the root-mean-square error between AMSR-E SICs and those of moderate resolution imaging spectroradiometer (MODIS) was 15%. In this study, SIE was defined as the sum of the areas where the AMSR-E SIC was >15%. When retrieving SIE, we adjusted the SIC threshold for each PMR to be consistent with the SIE calculated based on the 15% SIC threshold for AMSR-E. We then calculated a time-series of the SIE trends over approximately 45 years using the adjusted SIE data. Therefore, we revealed the dramatic decrease in global sea ice extent since 1978. This technique enables retrieval of more accurate long-term sea ice trends for more than half a century in the future.

Unravelling the impacts of stratospheric intrusions on near-surface ozone during the springtime ozone pollution episodes in Lhasa, China

While air pollution due to fine particulate matter (PM2.5) has been effectively controlled in China; the photochemical pollution characterized by elevated ozone (O3) has emerged as a major concern for air quality improvement. Except for ozone, Lhasa is one of the cleanest cities in China with the lowest annual PM2.5 concentration in 2017. The levels of major air pollutants in Lhasa are much lower than those of other cities in the eastern region of China, especially in May, when the O3 concentration peaks. This study was based on multi-source observations in combination with the Goddard Earth Observing System coupled with chemistry (GEOS-Chem) and the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) models to explore the causes of O3 pollution in Lhasa in May 2023. The results indicated that during the high O3 episodes, the concentrations of O3 precursors were low in Lhasa. Surrounding cities in other parts of the Tibetan Plateau also experienced high ozone concentrations despite being in different airsheds, suggesting that the O3 pollution in Lhasa was caused by regional transport rather than purely local emissions. Stratospheric intrusion events modulated by the westerly jet led to elevated ozone in the troposphere above Lhasa City. The results of the GEOS-Chem model indicated that horizontal advection, turbulence, diffusion, and other effects led to high concentrations of ozone in the near-surface above Lhasa. Vertical transport was the dominant factor leading to ozone concentration increases during high ozone days, with a contribution of 6.33 Gg/day. In addition, high-altitude air masses with a maximum altitude of over 8000 m, were observed arriving in Lhasa during the high ozone days. This study revealed that stratospheric intrusions have a greater contribution to the high O3 concentration in Lhasa in spring and provided a scientific basis for mitigating O3 pollution in the plateau cities.

Tropical Cyclones in the GEOS-S2S-2 Subseasonal Forecasts

Abstract This paper analyzes the climatology, prediction skill, and predictability of tropical cyclones (TCs) in NASA’s Goddard Earth Observing System Subseasonal to Seasonal (GEOS-S2S) forecast system version 2. GEOS reasonably simulates the number and spatial distribution of TCs compared to observations except in the Atlantic where the model simulates too few TCs due to low genesis rates in the Caribbean Sea and Gulf of Mexico. The environmental conditions, diagnosed through a genesis potential index, do not clearly explain model biases in the genesis rates, especially in the Atlantic. At the storm scale, GEOS reforecasts replicate several key aspects of the thermodynamic and dynamic structures of observed TCs, such as a warm core and the secondary circulation. The model, however, fails to simulate an off-center eyewall when evaluating vertical velocity, precipitation, and moisture. The analysis of the prediction skill of TC genesis and occurrence shows that GEOS has comparable skill to other global models in WMO S2S archive and that its skill could be further improved by increasing the ensemble size. After calibration, GEOS forecasts are skillful in the western North Pacific and southern Indian Ocean up to 20 days in advance. A model-based predictability analysis demonstrates the importance of the Madden–Julian oscillation (MJO) as a source of predictability of TC occurrence beyond the 14-day lead time. Forecasts initialized under strong MJO conditions show evidence of predictability beyond week 3. However, due to model biases in the forecast distribution, there are notable gaps between the MJO-related prediction skill and predictability, which require further study.

Gestational exposure to wildfire PM2.5 and its specific components and the risk of gestational hypertension and eclampsia in the southwestern United States

In the southwestern United States, the frequency of summer wildfires has elevated ambient PM2.5 concentrations and rates of adverse birth outcomes. Notably, hypertensive disorders in pregnancy (HDP) constitute a significant determinant associated with maternal mortality and adverse birth outcomes. Despite the accumulating body of evidence, scant research has delved into the correlation between chemical components of wildfire PM2.5 and the risk of HDP. Derived from data provided by the National Center for Health Statistics, singleton births from >2.68 million pregnant women were selected across 8 states (Arizona, AZ; California, CA, Idaho, ID, Montana, MT; Nevada, NV; Oregon, OR; Utah, UT, and Wyoming, WY) in the southwestern US from 2001 to 2004. A spatiotemporal model and a Goddard Earth Observing System chemical transport model were employed to forecast daily concentrations of total and wildfire PM2.5-derived exposure. Various modeling techniques including unadjusted analyses, covariate-adjusted models, propensity-score matching, and double robust typical logit models were applied to assess the relationship between wildfire PM2.5 exposure and gestational hypertension and eclampsia. Exposure to fire PM2.5, fire-sourced black carbon (BC) and organic carbon (OC) were associated with an augmented risk of gestational hypertension (ORPM2.5 = 1.125, 95 % CI: 1.109,1.141; ORBC = 1.247, 95 % CI: 1.214,1.281; OROC = 1.153, 95 % CI: 1.132, 1.174) and eclampsia (ORPM2.5 = 1.217, 95 % CI: 1.145,1.293; ORBC = 1.458, 95 % CI: 1.291,1.646; OROC = 1.309, 95 % CI: 1.208,1.418) during the pregnancy exposure window with the strongest effect. The associations were stronger that the observed effects of ambient PM2.5 in which the sources primarily came from urban emissions. Social vulnerability index (SVI), education years, pre-pregnancy diabetes, and hypertension acted as effect modifiers. Gestational exposure to wildfire PM2.5 and specific chemical components (BC and OC) increased gestational hypertension and eclampsia risk in the southwestern United States.

Enhancing long-term trend simulation of the global tropospheric hydroxyl (TOH) and its drivers from 2005 to 2019: a synergistic integration of model simulations and satellite observations

Abstract. The tropospheric hydroxyl (TOH) radical is a key player in regulating oxidation of various compounds in Earth's atmosphere. Despite its pivotal role, the spatiotemporal distributions of OH are poorly constrained. Past modeling studies suggest that the main drivers of OH, including NO2, tropospheric ozone (TO3), and H2O(v), have increased TOH globally. However, these findings often offer a global average and may not include more recent changes in diverse compounds emitted on various spatiotemporal scales. Here, we aim to deepen our understanding of global TOH trends for more recent years (2005–2019) at 1×1°. To achieve this, we use satellite observations of HCHO and NO2 to constrain simulated TOH using a technique based on a Bayesian data fusion method, alongside a machine learning module named the Efficient CH4-CO-OH (ECCOH) configuration, which is integrated into NASA's Goddard Earth Observing System (GEOS) global model. This innovative module helps efficiently predict the convoluted response of TOH to its drivers and proxies in a statistical way. Aura Ozone Monitoring Instrument (OMI) NO2 observations suggest that the simulation has high biases for biomass burning activities in Africa and eastern Europe, resulting in a regional overestimation of up to 20 % in TOH. OMI HCHO primarily impacts the oceans, where TOH linearly correlates with this proxy. Five key parameters, i.e., TO3, H2O(v), NO2, HCHO, and stratospheric ozone, can collectively explain 65 % of the variance in TOH trends. The overall trend of TOH influenced by NO2 remains positive, but it varies greatly because of the differences in the signs of anthropogenic emissions. Over the oceans, TOH trends are primarily positive in the Northern Hemisphere, resulting from the upward trends in HCHO, TO3, and H2O(v). Using the present framework, we can tap the power of satellites to quickly gain a deeper understanding of simulated TOH trends and biases.

Developing the Svalbard Integrated Arctic Earth Observing System (SIOS)

Abstract. Based on the ongoing large climatic and environmental changes and the history of science coordination in Svalbard leading to the development of Svalbard Integrated Arctic Earth Observing System (SIOS), we present an overview of the current gaps in knowledge and infrastructure based on a synthesis of the recommendations presented in the annual State of Environmental Science in Svalbard (SESS) reporting of SIOS. Recommendations from the first 4 years of SESS reporting represent the point of view of the wide scientific community operating the large observing system implemented in Svalbard (SIOS) since 2018 and aim to identify the scientific potential to further develop the observing system. The recommendations are bottom-up inputs for a continuous process that aims to accomplish the vision and mission of SIOS: optimizing, integrating and further developing the observing system in an Earth system science (ESS) perspective. The primary outcome of the synthesis work is the evidence that ESS in SIOS has, during the first 4 years of operation, naturally developed from individual scientists or smaller groups of scientists to larger disciplinary international groups of scientists working together within the different environments (the atmosphere, the cryosphere, and marine and terrestrial environments). It is clear that strategic efforts towards interdisciplinarity are necessary for operating fully at ESS scale in Svalbard. As Svalbard is experiencing the largest ongoing warming in the Arctic and worldwide, SIOS is in a unique position to perform a full-scale study of all processes impacting ESS dynamics and controlling the water cycle using all parts of the SIOS observation network, with a large potential for increasing the understanding of key mechanisms in the Earth system. We also identify the potential to upscale Svalbard-based observations collected in SIOS to pan-Arctic and global scales, contributing to full-scale ESS.

Assessing soil degradation in Brazilian agriculture by a remote sensing approach to monitor bare soil frequency: impact on soil carbon

In countries with extensive agricultural practices, there is a significant risk of soil degradation, making it essential to develop techniques for understanding and detecting these changes. In this study, we used an earth observation system to identify the temporal bare soil frequency and thus, relate it with soil tillage and its impact on soil carbon degradation. The work was performed in two important agricultural states of Brazil, São Paulo and Paraná. For that, historical field and Remote Sensing (RS) data were analyzed to identify the relation between bare soil areas and their degradation. The frequency of bare soil was detected by Landsat images, in the last 36 years using the Geospatial Soil Sensing System (GEOS3). Historical soil surface temperature data was produced using the same images. In addition, legacy pedological and crops (i.e., soil cover) maps were used. Finally, soil texture information was spatialized based on a synthetic soil image (SISY). A total of 28,000 sites with topsoil organic carbon (SOC) were used as the reference for degradation. The soils of the state of Paraná presented significantly lower bare soil areas when compared to the state of São Paulo, mainly due to the wide use of the No-Tillage system. The advancement of sugarcane harvesting technologies together with the "boom" of the commodities after 2000s was responsible for the considerable increases in soil cover conservation. It was noticed that the more exposed the soil remains, the less carbon it has, having a negative correlation (r≈ −0.5). Sandy soils in both states proved to be the ones that were subject to the highest exposure rates and thus, more degraded. This fact is of concern, given that sandy soils are more susceptible to degradation factors, such as erosion. We observed important historical public policies related to the temporal tillage systems adopted by agro-community, which had a significant impact on carbon dynamics. The remote technique was able to infer how the soil has been managed. This information is crucial as it provides a solid basis for developing future public policies aimed at sustainable production.

A Potential Surface Warming Regime for Volcanic Super‐Eruptions Through Stratospheric Water Vapor Increases

AbstractLarge volcanic eruptions are known to influence the climate through a variety of mechanisms including aerosol‐forced cooling and warming via emitted CO2. The January 2022 Hunga shallow underwater eruption caused an increase in stratospheric water vapor, and demonstrated how the associated positive radiative forcing can be an important component of an eruption's climate forcing. We present interactive stratospheric aerosol model simulations of super‐volcanic eruptions with a range of SO2 emissions that can produce climate warming through feedback effects produced by a large igneous province (or “flood basalt”) mid‐latitude super‐eruption using Goddard Earth Observing System Chemistry Climate Model climate model simulations. The model experiments suggest total SO2 emissions ≳4,000 Tg/4 Gt generate a multi‐year period of sustained aerosol absorptive local‐heating of the upper troposphere and lower stratosphere and hence produce net climate warming after strong initial cooling. The eruptions produce stratospheric water vapor increases of factors of 8–600. The initiation of these feedbacks within the simulations suggest they could occur for individual stratovolcano eruptions of the scale of the Toba or Tambora eruptions. We note the sensitivity of our results to volcanic sulfate aerosol microphysics and model chemistry.

Low-Cost Hourly Ambient Black Carbon Measurements at Multiple Cities in Africa.

There is a notable lack of continuous monitoring of air pollutants in the Global South, especially for measuring chemical composition, due to the high cost of regulatory monitors. Using our previously developed low-cost method to quantify black carbon (BC) in fine particulate matter (PM2.5) by analyzing reflected red light from ambient particle deposits on glass fiber filters, we estimated hourly ambient BC concentrations with filter tapes from beta attenuation monitors (BAMs). BC measurements obtained through this method were validated against a reference aethalometer between August 2 and 23, 2023 in Addis Ababa, Ethiopia, demonstrating a very strong agreement (R2 = 0.95 and slope = 0.97). We present hourly BC for three cities in sub-Saharan Africa (SSA) and one in North America: Abidjan (Côte d'Ivoire), Accra (Ghana), Addis Ababa (Ethiopia), and Pittsburgh (USA). The average BC concentrations for the measurement period at the Abidjan, Accra, Addis Ababa Central summer, Addis Ababa Central winter, Addis Ababa Jacros winter, and Pittsburgh sites were 3.85 μg/m3, 5.33 μg/m3, 5.63 μg/m3, 3.89 μg/m3, 9.14 μg/m3, and 0.52 μg/m3, respectively. BC made up 14-20% of PM2.5 mass in the SSA cities compared to only 5.6% in Pittsburgh. The hourly BC data at all sites (SSA and North America) show a pronounced diurnal pattern with prominent peaks during the morning and evening rush hours on workdays. A comparison between our measurements and the Goddard Earth Observing System Composition Forecast (GEOS-CF) estimates shows that the model performs well in predicting PM2.5 for most sites but struggles to predict BC at an hourly resolution. Adding more ground measurements could help evaluate and improve the performance of chemical transport models. Our method can potentially use existing BAM networks, such as BAMs at U.S. Embassies around the globe, to measure hourly BC concentrations. The PM2.5 composition data, thus acquired, can be crucial in identifying emission sources and help in effective policymaking in SSA.

Subseasonal Forecast Skill of Evaporative Demand, Soil Moisture, and Flash Drought Onset from Two Dynamic Models over the Contiguous United States

Abstract Flash droughts are rapidly developing subseasonal climate extreme events that are manifested as suddenly decreased soil moisture, driven by increased evaporative demand and/or sustained precipitation deficits. Over each climate region in the contiguous United States (CONUS), we evaluated the forecast skill of weekly root-zone soil moisture (RZSM), evaporative demand (ETo), and relevant flash drought (FD) indices derived from two dynamic models [Goddard Earth Observing System model V2p1 (GEOS-V2p1) and Global Ensemble Forecast System version 12 (GEFSv12)] in the Subseasonal Experiment (SubX) project between years 2000 and 2019 against three reference datasets: Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), North American Land Data Assimilation System, phase 2 (NLDAS-2), and GEFSv12 reanalysis. The ETo and its forcing variables at lead week 1 have moderate-to-high anomaly correlation coefficient (ACC) skill (∼0.70–0.95) except downwelling shortwave radiation, and by weeks 3–4, predictability was low for all forcing variables (ACC < 0.5). RZSM (0–100 cm) for model GEFSv12 showed high skill at lead week 1 (∼0.7–0.85 ACC) in the High Plains, West, Midwest, and South CONUS regions when evaluated against GEFSv12 reanalysis but lower skill against MERRA-2 and NLDAS-2 and ACC skill are still close to 0.5 for lead weeks 3–4, better than ETo forecasts. GEFSv12 analysis has not been evaluated against in situ observations and has substantial RZSM anomaly differences when compared to NLDAS-2, and our analysis identified GEFSv12 reforecast prediction limit, which can maximally achieve ACC ∼0.6 for RZSM forecasts between lead weeks 3 and 4. Analysis of major FD events reveals that GEFSv12 reforecast inconsistently captured the correct location of atmospheric and RZSM anomalies contributing to FD onset, suggesting the needs for improving the dynamic models’ assimilation and initialization procedures to improve subseasonal FD predictability. Significance Statement Flash droughts are rapidly developing climate extremes which reduce soil moisture through enhanced evaporative demand and precipitation deficits, and these events can have large impacts on the ecosystem and crop health. We evaluated the subseasonal forecast skill of soil moisture and evaporative demand against three reanalysis datasets and found that evaporative demand skill was similar between forecasts and reanalyses while soil moisture skill is dependent on the reference dataset. Skill of evaporative demand decreases rapidly after week 1, while soil moisture skill declines more slowly after week 1. Case studies for the 2012, 2017, and 2019 United States flash droughts identified that forecasts could capture rapid decreases in soil moisture in some regions but not consistently, implying that long-lead forecasts still need improvements before being used in early warning systems. Improvements in flash drought predictability at longer lead times will require less biased initial conditions, better model parameterizations, and improved representations of large-scale teleconnections.

Comparison of datasets citation coverage in Google Scholar, Web of Science, Scopus, Crossref, and DataCite

The rapid increase of Earth science data from remote sensing, models, and ground-based observations highlights an urgent need for effective data management practices. Data repositories track provenance and usage metrics which are crucial for ensuring data integrity and scientific reproducibility. Although the introduction of Digital Object Identifiers (DOIs) for datasets in the late 1990s has significantly aided in crediting creators and enhancing dataset discoverability (akin to traditional research citations), considerable challenges persist in establishing linkage of datasets used with scholarly documents. This study evaluates the citation coverage of datasets from NASA’s Earth Observing System Data and Information System (EOSDIS) across several major bibliographic sources ‒ namely Google Scholar (GS), Web of Science (WoS), Scopus, Crossref, and DataCite—which helps data managers in making informed decisions when selecting bibliographic sources. We provide a robust and comprehensive understanding of the citation landscape, crucial for advancing data management practices and advancing open science. Our study searched and analyzed temporal trends across the bibliographic sources for publications that cite approximately 11,000 DOIs associated with EOSDIS datasets, yielding 17,000 unique journal and conference articles, reports, and book records linked to 3,000 dataset DOIs. GS emerged as the most comprehensive source while Crossref lagged significantly behind the other major sources. Crossref’s record references revealed that the absence of dataset DOIs and shortcomings in the Crossref Event data interface likely contributed to its underperformance. Scopus initially outperformed WoS until 2020, after which WoS began to show superior performance. Overall, our study underscores the necessity of utilizing multiple bibliographic sources for citation analysis, particularly for exploring dataset-to-document connections.

Analysis of Seismic Methane Anomalies at the Multi-Spatial and Temporal Scales

Relevant studies have shown that methane gas has a close relationship with seismic activity. The concentration of methane released within a tectonic zone can reflect the intensity status of tectonic activities, which is important for seismic monitoring. In this study, the January 2020 Xinjiang Jiashi earthquake was taken as the research object, and the mature Robust Satellite Technique (RST) algorithm was used to characterize the L3-level methane product data from the hyperspectral sensor, Atmospheric Infrared Sounder (AIRS), installed on the Earth Observing System (EOS) AQUA satellite at the monthly scale, 8-day scale and daily scale. An analysis of the spatial and temporal distribution of methane was carried out for before and after the earthquake based on the 3D structural condition of the gas, and the 3D structural conditions of the 8-day scale were introduced. An 8-day scale 3D structural condition was introduced and migration validation was performed, and the results showed that (1) the seismic methane anomaly-extraction process proposed in this study is feasible; (2) the 3D contour features indicated that the methane anomalies that occurred before the Jiashi earthquake were caused by geogenic emissions; (3) the anomaly-extraction algorithm from this study did not extract the corresponding anomalies in the non-seismic year, which indicated that the anomaly-extraction algorithm of this study has some degree of feasibility; and (4) the migrated validation of the Wenchuan earthquake of May 2008 further suggested that methane anomalies at the time of the Wenchuan earthquake were caused by the earthquake.

Geoinformation Technology in Support of Arctic Coastal Properties Characterization: State of the Art, Challenges, and Future Outlook

Climate change is increasingly affecting components of the terrestrial cryosphere with its adverse impacts in the Arctic regions of our planet are already well documented. In this context, it is regarded today as a key scientific priority to develop methodologies and operational tools that can assist towards advancing our monitoring capabilities and improving our decision-making competences in Arctic regions. In particular, the Arctic coasts are the focal point in this respect, due to their strong connection to the physical environment, society, and the economy in such areas. Geoinformation, namely Earth Observation (EO) and Geographical Information Systems (GISs), provide the way forward towards achieving this goal. The present review, which to our knowledge is the first of its kind, aims at delivering a critical consideration of the state-of-the-art approaches exploiting EO datasets and GIS for mapping the Arctic coasts properties. It also furnishes a reflective discussion on the scientific gaps and challenges that exist that require the attention of the scientific and wider community to allow exploitation of the full potential of EO/GIS technologies in this domain. As such, the present study also serves as a valuable contribution towards pinpointing directions for the design of effective policies and decision-making strategies that will promote environmental sustainability in the Arctic regions.

Utilizing Data-Driven Approaches to Forecast Fluctuations in Groundwater Table

Accurate forecasting of fluctuations in groundwater table is crucial for the effective management of regional water resources. This study explores the potential of utilizing remotely sensed satellite data to predict and forecast water table variations. Specifically, two Artificial Neural Network (ANN) models were developed to simulate water table fluctuations at two distinct well sites, namely BA Ea 18 and FR Df 35 in Maryland. One model leveraged the relationship between variations in brightness temperature and water table depth, while the other model was founded on the association between changes in soil moisture and water table depth. These models were trained and validated using recorded water table depths from the aforementioned wells, brightness temperature data acquired from the Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E), and soil moisture information generated using the Land Data Assimilation System (LDAS). All models exhibited strong performance in predicting and forecasting water table fluctuations, with root mean square errors ranging from 0.043 m to 0.047 m for a 12-month forecasting horizon. Sensitivity tests revealed that the models displayed greater sensitivity to uncertainties in water table depth compared to uncertainties in both brightness temperature and soil moisture content. This underscores the feasibility of constructing an ANN-based water table prediction model, even in cases where high-resolution remotely sensed data is unavailable. In such situations, the model’s efficacy is contingent on the compatibility of the time series trends in data, such as brightness temperature or soil moisture, with those observed at the study site.

Positioning Improvement for Spaceborne Laser Footprint Based on Precisely Terrain Data

Abstract. Spaceborne laser altimetry represents a novel active remote sensing technology applicable to earth observation, which together with imaging spectroscopy and synthetic aperture radar as a core technology for data acquisition in the earth observation systems. However, the accuracy of horizontal positioning for laser footprints from spaceborne laser altimeters declines due to various factors such as the changes in the orbital environment and the deterioration of performance. Moreover, the limited frequency of in-orbit calibration of the spaceborne laser altimeters and the non-disclosure of calibration parameters mean that users are heavily reliant on positioning accuracy of the altimetry data provided. To address this issue, a new algorithm is proposed in this study for enhancing the accuracy of horizontal positioning for laser footprints in the absence of satellite altimeter pointing and ranging parameters. In this algorithm, high-resolution DSM is taken as the reference terrain data to take advantage of the higher precision in elevation over horizontal positioning of the laser footprints. By adjusting the horizontal position of the laser footprint within a small area, the algorithm achieves the optimal alignment of laser elevation data with the reference terrain. Then, the resulting shift in the horizontal position of the laser footprints is referenced to correct their horizontal positioning during that period. Based on the high-accuracy DSM data collected from the Xinjiang autonomous region in China and the data collected by the GF-7 satellite, simulation experiments are performed in this study to analyze and validate the proposed algorithm. According to the experimental results, the horizontal accuracy of the laser footprints improves significantly from 12.56 m to 3.11 m after optimization by the proposed method. With the elimination of 9.45 m horizontal error, accuracy is improved by 75.23%. This method is demonstrated as effective in further optimizing the horizontal position of laser altimetry data products in the absence of altimeter parameters and original data, which promotes the application of spaceborne laser data.

Land Surface Temperature Retrieval From GF5-01A Based on Split- Window Algorithm

Abstract. Land Surface temperature (LST) is a core parameter in the energy exchange between the surface and the atmosphere, and the use of thermal infrared remote sensing can realize the wide-range, fast, and accurate acquisition of surface temperature. GF5-01A is an important part and the final satellite of the major special project on high-resolution Earth observation system which is equipped with a wide-area thermal infrared imager with a resolution of 100 meters and a width of 1,500 kilometers. In this paper, based on the GF5-01A WTI spectral response function, combined with the TIGR2000 atmospheric profiles data and the ASTER spectral library, the data simulation was carried out by using the atmospheric radiative transfer model MODTRAN 5.2, and then constructed the split-window algorithm. Then, the method proposed in this paper was validated and evaluated using Landsat 8/9 temperature products and measured surface temperature data from SURFRAD sites acquired on the same day. The results show that the RMSE between the GF5-01A retrieved LST and the Landsat8/9 retrieved LST is between 1.27–2.24K, and the Bias is between −2.08–1.12K. The RMSE is between 0.68–2.64K and the bias is between −0.68–1.49K compared to the measured surface temperature. The split-window algorithm of GF5-01A proposed in this paper can meet the requirements of thermal infrared remote sensing monitoring and has enormous potential and value.

CONVCAT: A NEW CLASSIFICATION APPROACH USING UC MERCED AND RESISC45 DATASETS

With advances in Earth observation systems, the importance of remote sensing data is increasing daily. These data are used in various fields ranging from image segmentation to terrain classification, from disaster impact assessment to climate change analysis. The use of remotely sensed images for terrain classification has been the subject of a number of studies. This study proposes a new method for terrain classification in the UC Merced Land Use Dataset and RESISC45 remote sensing images. This method is called ConvCat model, which is a combination of classical convolutional layer and CatBoost models. The performance of this model is measured in terms of accuracy, the Matthews Correlation Coefficient (MCC) and the Cohen's Kappa metrics. The results are compared with ensemble models (XGBoost, CatBoost), with ConvXGB, a combination of convolutional learning and XGBoost, and with ResNet50, one of the most widely used transfer learning models. The developed ConvCat model outperformed the other models, achieving an accuracy of 97.44% on the UC Merced data set and an accuracy of 96.89% on the Resisc45 data set. This study shows that our newly developed model provides the best results for the classification problem based on remote sensing images.

Contributions of Initial Conditions and Meteorological Forecast to Subseasonal-to-Seasonal Hydrological Forecast Skill in Western Tropical South America.

Hydrological predictions at subseasonal-to-seasonal (S2S) time scales can support improved decision-making in climate-dependent sectors like agriculture and hydropower. Here, we present an S2S hydrological forecasting system (S2S-HFS) for western tropical South America (WTSA). The system uses the global NASA Goddard Earth Observing System S2S meteorological forecast system (GEOS-S2S) in combination with the generalized analog regression downscaling algorithm and the NASA Land Information System (LIS). In this implementation study, we evaluate system performance for 3-month hydrological forecasts for the austral autumn season (March-May) using ensemble hindcasts for 2002-17. Results indicate that the S2S-HFS generally offers skill in predictions of monthly precipitation up to 1-month lead, evapotranspiration up to 2 months lead, and soil moisture content up to 3 months lead. Ecoregions with better hindcast performance are located either in the coastal lowlands or in the Amazon lowland forest. We perform dedicated analysis to understand how two important teleconnections affecting the region are represented in the S2S-HFS: El Niño-Southern Oscillation (ENSO) and the Antarctic Oscillation (AAO). We find that forecast skill for all variables at 1-month lead is enhanced during the positive phase of ENSO and the negative phase of AAO. Overall, this study indicates that there is meaningful skill in the S2S-HFS for many ecoregions in WTSA, particularly for long memory variables such as soil moisture. The skill of the precipitation forecast, however, decays rapidly after forecast initialization, a phenomenon that is consistent with S2S meteorological forecasts over much of the world.