Satellite Data Research Articles

A study of rainfall thresholds for landslides in Badung Regency using satellite-derived rainfall grid datasets

Integrating field rainfall data with satellite data improves data accuracy and overcomes rainfall data limitations for rain thresholds. Integration can involve field rainfall data, satellite rainfall data, or a different satellite dataset. Merging these rainfall data sources provides more spatial coverage of satellite data. To determine how well rainfall thresholds predict rainfall-triggered landslides, the threshold model must be validated. This study will evaluate satellite rainfall data before and after integration in developing a rainfall threshold model for landslide prediction in Badung Regency. To do so, the study used a cumulative rainfall threshold over 3, 7, 15, and 30 days and two rainfall satellite products (integrated merged multi-satellite retrievals (IMERG) and precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN)). Median, first, and third quartiles were used to set thresholds. The area under the curve (AUC) was calculated to validate rainfall threshold outcomes using receiver operating characteristic (ROC) curves. Analysis showed that integrating satellite rainfall data into the rainfall threshold model for landslide prediction yields better results than other methods. An AUC value of 0.903 (90.3%) for the 30-day cumulative rainfall thresholds supports this claim. This model could be a good input for a landslide early warning system in Badung Regency.

Advanced utilization of satellite and governmental data for determining the coverage and condition of green areas in Poland: An experimental statistics supporting the Statistics Poland

Statistics Poland utilizes Earth Observation and in-situ data to assess green areas, aiming to derive vital information on natural capital statistics and human well-being. This is essential for informed decision-making and sustainable development. Improving regional well-being statistics is a priority due to Poland's low rankings in environmental well-being indicators. To overcome challenges in obtaining comprehensive data at the commune level, the study introduces a new and innovative system for monitoring green areas. This system employs Sentinel-2 satellite data, combined with Copernicus vegetation and phenology products in order to determine extent and condition of green areas. In addition, the applied land cover product (Dynamic World) and national vector database of topographic features (BDOT10k) contributes to the quality and accuracy of the analyses carried out. The system yields two pivotal indicators: one delineating the proportional coverage of green areas within communes boundaries, and the other elucidating the state and health of these delineated areas. A key highlight of the system is its innovative use of local resources and the Google Earth Engine application to observe green areas at a 10-meter resolution at the commune level, particularly in major cities in Poland. These observations are crucial for the Polish Public Services Monitoring System (SMUP), providing comprehensive local-level evaluation services. The research outcome consists of a comparison between the extent of green areas delineated by public administration at the commune level and the extent of green areas determined using a newly proposed method, which relies on satellite imagery from the Copernicus program. The results indicate variability between these two datasets, depending on different forms of land cover within each administrative unit. The most consistent data is derived from large urban agglomerations in Poland.

A Hybrid Experimental Modelling Approach to Solar Photovoltaic Cell Temperature Prediction

Solar cell temperature is critical in the determination of solar energy generated by a solar photovoltaic power plant. High temperatures are associated with a reduction in the energy generated and hence prediction of photovoltaic cell temperature is essential in temperature mitigation and solar energy forecasting especially in commercial power plants. The present study focused on the development of a machine learning based predictive model for solar photovoltaic cell temperature prediction in commercial solar photovoltaic power plants. A physical experimental set up was developed to measure solar cell temperature under different weather and other related parameters. Satellite data were also collated for those parameters difficult to measure experimentally and were used to compliment experimental data used in this study. Satellite data used in the study were statistically transformed for each parameter used to mimic experimentally measured data. Statistical approaches were adopted to analyse the influence of both the dependent and independent variables on solar cell temperature. The analysis included multicollinearity and correlation analysis with the aid of heat maps meant to establish the relationships among the independent and dependent variables. Feature selection and dimensionality reduction was also performed to reduce the input variables and maintain relevant data in the modelling process. Parameters with a strong correlation to each other had some of them eliminated in the modelling process. A solar cell temperature predictive model based on selected weather parameters was developed using a machine learning approach (Random Forests), and parameters used were selected from the statistical analysis. The prediction accuracy of the developed model was analysed using the coefficient of determination (R2) and the mean absolute percentage error (MAPE). The results indicated a higher model performance compared to generic models used in cell temperature prediction. The prediction MAPE for the developed model was 0.83 °C while an R2 value of 0.93 was obtained which was indicative of a good model. The developed model was also comparable to other contemporary models developed to predict solar photovoltaic cell temperature. Simulations were also done to determine the annual energy generated with the incorporation of the solar cell temperature prediction model. The results revealed a 3.4% difference in the annual energy generated between a simulation which considered solar cell temperature and that which ignored the solar cell temperature. The study also revealed that this difference is even larger in monetary terms when lifetime energy generation is considered. The present study demonstrated that Random Forests, a machine learning approach to predictive modelling, can handle complex models and can provide models with a higher accuracy compared to statistical modelling approaches. The study recommends the use of solar cell predictive models to improve the accuracy of energy prediction on solar photovoltaic power plants which in turn assists in energy planning and deployment.

Predictive models for photosynthetic active radiation irradiance in temperate climates

This research evaluated 10 different empirical models designed for predicting Photosynthetically Active Radiation (PAR) at higher latitudes, addressing atmospheric conditions specific to these regions. The research introduces the Musleh-Rahman (MR) model, which substitutes Diffuse Horziontal Irradiance (DHI) with Clear Direct Normal Irradiance (DNI), Ozone and Aerosol Optical Depth at 550 nm (AOD550) sourced for satellite reanalysis data, achieving a Mean Bias Deviation (MBD) of 0.19 % and Root Mean Square Error (RMSE) of 12.42 W/m2. Furthermore, when applied to six untested locations, results demonstrate that the MR model outperformed the best performing empirical model with an MBD improvement of 3.68 % and an RMSE of 4.28 W/m2, whereas, when compared to machine learning models, the Light Gradient Boost Model (LGBM), had an MBD of −3.85 %. The MR model also maintained consistency across seasonal and density evaluations, attaining an R2 value as high as 0.9709, thereby highlighting the significant benefits of integrating satellite-sourced atmospheric data into PAR prediction models. Moreover, the research illustrated that substituting DHI with Clear DNI, Ozone, and AOD550 not only reduces MBD and boosts R2 values but also amplifies the model's applicability and accuracy in capturing early PAR peaks and reducing overestimations through precise adjustments in Ozone and AOD550 levels. This highlights the benefits of incorporating satellite-derived atmospheric data into PAR predictions models.

A new model for plasmapause locations derived from IMAGE RPI and Van Allen probe data

The outer boundary of the plasmasphere, the plasmapause, is characterized by a sharp electron density gradient that changes under varying space weather conditions. We developed a new model, called Neustrelitz/ESOC PlasmaPause Model (NEPPM), for providing plasmapause location in terms of -shell utilizing electron density measurements from the Van Allen probes from 2012 to 2018 and the IMAGE satellite data from 2001 to 2005. Both datasets were preprocessed, and algorithms were developed for automatic detection of plasmapause location. The suggested model provides a simple ellipse-based approach determined by the semi-major axis, the eccentricity, and the orientation angle of the semi-major axis. The modelled varies as a function of the index and magnetic local time . The NEPPM results are compared with the Global Core Plasma Model (GCPM). The plasmapause bulge in the evening hours follows the level of geomagnetic activity. The NEPPM will complete the NPSM (Neustrelitz PlasmaSphere Model), which was derived from dual-frequency GPS measurements on-board the CHAMP satellite mission.

The impact of carbon emission trading schemes on China’s economic inequalities between and within urban and rural areas based on the satellite data: a county-level analysis

ABSTRACT Although China’s carbon emission trading schemes (CETS) garnered significant attention in the field of climate mitigation, limited data availability has hindered exploration into the impact of CETS on economic inequality at the county level in China. Given its potential to facilitate industrial upgrading and generate employment opportunities, the adoption of CETS holds promise for reducing economic inequality. To address data limitations, we employed a combination of gridded nighttime light data and land use data to estimate China’s county-level Dagum Gini coefficient and its components. Subsequently, we applied a difference-in-difference-in-difference method to examine how CETS affected economic inequalities between and within urban and rural areas in China. Our findings reveal that: (1) CETS significantly reduced overall economic inequality in counties with a high proportion of secondary industry output; (2) this reduction primarily occurred within urban and rural areas rather than between them. Further heterogeneity analysis demonstrates that while CETS effectively mitigated economic inequality between urban and rural areas in central and western regions, it did not achieve similar outcomes in the eastern region; (3) service industry development and rural GDP per capita served as important channels through which CETS reduced economic inequality within rural areas. Infrastructure development driven by CETS primarily addressed economic inequality within urban areas rather than rural areas.

Loom: A Modular Open-Source Approach to Rapidly Produce Sensor, Actuator, Datalogger Systems

In the face of rising population, erratic climate, resource depletion, and increased exposure to natural hazards, environmental monitoring is increasingly important. Satellite data form most of our observations of Earth. On-the-ground observations based on in situ sensor systems are crucial for these remote measurements to be dependable. Providing open-source options to rapidly prototype environmental datalogging systems allows quick advancement of research and monitoring programs. This paper introduces Loom, a development environment for low-power Arduino-programmable microcontrollers. Loom accommodates a range of integrated components including sensors, various datalogging formats, internet connectivity (including Wi-Fi and 4G Long Term Evolution (LTE)), radio telemetry, timing mechanisms, debugging information, and power conservation functions. Additionally, Loom includes unique applications for science, technology, engineering, and mathematics (STEM) education. By establishing modular, reconfigurable, and extensible functionality across components, Loom reduces development time for prototyping new systems. Bug fixes and optimizations achieved in one project benefit all projects that use Loom, enhancing efficiency. Although not a one-size-fits-all solution, this approach has empowered a small group of developers to support larger multidisciplinary teams designing diverse environmental sensing applications for water, soil, atmosphere, agriculture, environmental hazards, scientific monitoring, and education. This paper not only outlines the system design but also discusses alternative approaches explored and key decision points in Loom’s development.

Comparison of the monitoring of surface deformations in open-pit mines with Sentinel-1A and TerraSAR-X satellite radar data.

In case necessary precautions are not taken in surface mines, serious accidents and loss of life may occur, particularly due to large mass displacements. It is extremely important to identify the early warning signs of these displacements and take the necessary precautions. In this study, free medium-resolution satellite radar images from the European Space Agency's (ESA) C-band Sentinel-1A satellite and commercial high-resolution satellite radar images (SAR, Synthetic Aperture Radar) from the Deutsches Zentrum für Luft- und Raumfahrt's (DLR) X-band TerraSAR-X satellite were obtained, and it was attempted to reveal the traceability and adequacy of monitoring of deformations and possible mass displacements in the dump site of an open-pit coal mine. The compatibility of the results obtained from the satellite radar data with two devices of Global Positioning System (GPS) which were installed in the field was evaluated. Furthermore, the velocity results in the Line Of Sight (LOS) direction and vertical deformation velocity results obtained with all three approaches (GPS/Sentinel-1A, GPS/TerraSAR-X, and Sentinel-1A/TerraSAR-X) were compared. It was observed that the results were statistically equal and the directions of movement were similar/compatible. The result of this study showed that deformations at mine sites can be monitored with sufficient accuracy for early warning with free Sentinel-1A satellite data, although the TerraSAR-X satellite offers a higher resolution.

Deforestation‐Driven Increases in Shallow Clouds Are Greatest in Drier, Low‐Aerosol Regions of Southeast Asia

AbstractAnthropogenic activity drives extensive tropical deforestation, particularly in Southeast Asia where 16% of total forest cover was lost between 2000 and 2020. While land surface changes significantly affect the atmosphere, their net impact on convective clouds is not well‐constrained. Here, we use satellite data to demonstrate long‐term deforestation in Southeast Asia robustly alters cloud properties and provide the first observational evidence that the magnitude of this response depends on the atmospheric environment. Deforestation drives a shift toward more widespread, shallower clouds during the daytime, with amplified effects in dry inland areas compared with moist coastal regions. Aerosols only weakly modulate the cloud fraction response, but offset the cloud top height response to deforestation, suggesting the influence of aerosol indirect effects. We conclude that the local signature of forest loss is not uniform, and regional differences in climatology must be considered when assessing deforestation impacts on clouds and the climate system.

Combined assimilation of NOAA surface and MIPAS satellite observations to constrain the global budget of carbonyl sulfide

Abstract. Carbonyl sulfide (COS), a trace gas in our atmosphere that leads to the formation of aerosols in the stratosphere, is largely taken up by terrestrial ecosystems. Quantifying the biosphere uptake of COS could provide a useful quantity to estimate gross primary productivity (GPP). Some COS sources and sinks still contain large uncertainties, and several top-down estimates of the COS budget point to an underestimation of sources, especially in the tropics. We extended the inverse model TM5-4DVAR to assimilate Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) satellite data, in addition to National Oceanic and Atmospheric Administration (NOAA) surface data as used in a previous study. To resolve possible discrepancies among the two observational data sets, a bias correction scheme is necessary and implemented. A set of inversions is presented that explores the influence of the different measurement streams and the settings of the prior fluxes. To evaluate the performance of the inverse system, the HIAPER Pole-to-Pole Observations (HIPPO) aircraft observations and NOAA airborne profiles are used. All inversions reduce the COS biosphere uptake from a prior value of 1053 GgS a−1 to much smaller values, depending on the inversion settings. These large adjustments of the biosphere uptake often turn parts of Amazonia into a COS source. Only inversions that exclusively use MIPAS observations, or strongly reduce the prior errors on the biosphere flux, maintain the Amazon as a COS sink. Inclusion of MIPAS data in the inversion leads to a better separation of land and ocean fluxes. Over the Amazon, these inversions reduce the biosphere uptake from roughly 300 to 100 GgS a−1, indicating a strongly overestimated prior uptake in this region. Although a recent study also reported reduced COS uptake over the Amazon, we emphasise that a careful construction of prior fluxes and their associated errors remains important. For instance, an inversion that gives large freedom to adjust the anthropogenic and ocean fluxes of CS2, an important COS precursor, also closes the budget satisfactorily with much smaller adjustments to the biosphere. We achieved better characterisation of biosphere prior and uncertainty, better characterisation of combined ocean and land fluxes, and better constraint of both by combining surface and satellite observations. We recommend more COS observations to characterise biosphere and ocean fluxes, especially over the data-poor tropics.

Satellite impact on astronomical observations based on the elliptical orbit model

Space-based and ground-based telescopes have extensively documented the impact of satellites on astronomical observations. With the proliferation of satellite mega-constellation programmes, their influence on astronomical observations has become undeniable. Quantifying the impact of satellites on telescopes is crucial. To this end, we enhanced the circular orbit model for satellites and introduced a methodology based on two-line element (TLE) orbit data. This involves constructing a satellite probability distribution model to evaluate the impact of satellites on telescopes. Using our method, we assessed the satellite impact on global grounded observatories. Our results indicate that the regions most severely affected by satellite interference at present are those near the equator, with latitudes of around $ and $ degrees experiencing the most significant impact from low-Earth-orbit satellites. Furthermore, we validated the reliability of our method using imaging data obtained from the focal surface acquisition camera of the LAMOST telescope.

Unraveling the characteristic spatial scale of habitat selection for forest grouse species in the boreal landscape

The characteristic spatial scale at which species respond strongest to forest structure is unclear and species-specific and depends on the degree of landscape heterogeneity. Research often analyzes a pre-defined spatial scale when constructing species distribution models relating forest variables with occupancy patterns. This is a limitation, as forest characteristics shape the species use of habitat at multiple spatial scales. To explore the drivers of this relationship, we conducted an in-depth investigation into how scaling forest variables at biologically relevant spatial scales affects occupancy of grouse species in boreal forest. We used 4,790 grouse observations (broods and adults) collected over 39,303 stands for 15 years of four forest grouse species (capercaillie, black grouse, hazel grouse, and willow grouse) obtained from comprehensive Finnish wildlife triangle census data and forest variables obtained from Airborne Laser Scanning and satellite data originally sampled at 16 m resolution. We fitted Generalized Additive Mixed Models linking grouse presence/absence in the Finnish boreal forest with forest stand structure and composition. We estimated the effects of predictor variables aggregated at three spatial scales reflecting the species use of the landscape: local level at stand scale, home range level at 1 km radius, and regional level at 5 km radius. Multi-grain models considering forest-species relationships at multiple scales were used to evaluate whether there is a specific scale at which forest characteristics best predict local grouse occupancy. We found that that the spatial scale affected the predictive capacity of the grouse occupancy models and the characteristic scale of habitat selection was the same (i.e., stand scale) among species. Different grouse species exhibited varying optimal spatial scales for occupancy prediction. Forest structure was more important than compositional diversity in predicting grouse occupancy irrespective of the scale. A limited number of forest predictors related to availability of multi-layered vegetation and of suitable thickets explained the occupancy patterns for all the grouse species at different scales. In conclusion, modeling grouse occupancy using forest predictors at different spatial scales can inform forest managers about the scale at which the species perceive the landscape. This evidence calls for an integrated multiscale approach to habitat modelling for forest species.

Estimating Evapotranspiration in the Qilian Mountains Using GRACE/GRACE-FO Satellite Data

Evapotranspiration (ET) is the most significant constituent of the response to climate warming. It serves as a crucial link in the soil–vegetation–atmospheric continuum. Analyzing the driving forces and response of ET to regional-scale climate warming holds scientific significance in improving global water resource assessment methods and drought monitoring techniques. The innovation presented in this article is the calculation of ET by using GRACE/GRACE-FO satellite data through the water balance equation. The inter-annual and seasonal changes in ET in different regions of the Qilian Mountains were analyzed, along with quantifying the contribution of environmental meteorological factors to ET. The ETGRACE and ETMonitor products have good consistency, with a monthly correlation coefficient of 0.92, an NSE coefficient of 0.80, and a root mean square error of 10.38 mm. The results indicate that the increasing trend of ET in the Qilian Mountains region exhibits a “medium–high–low” distribution pattern. The rate of increase in ET is 5.2 mm/year in the central segment. In spring and summer, the overall trend of ET is an increasing one. However, the central and western segments exhibit a slight decreasing trend of ET in autumn. During winter, the southern part of the Qilian Mountains experiences a notable reduction in ET. The correlation between the changes in ET and soil moisture exhibited a strong association, with soil moisture change contributing significantly to ET: 57.8% for the eastern section, 52.8% for the middle section, and 46.9% for the western section. The thermal effect primarily controls ET variations within eastern sections, where temperature change accounts for approximately 6.7% of the total variation in ET levels. Conversely, the moisture factor dominates western sections, where precipitation change accounts for about 6.5% of the total variation in ET levels. Due to the distinct gradient characteristics of environmental meteorological factors in the central segment, the fluctuation of these factors collaboratively drives ET changes. This article provides a new approach for obtaining continuous and reliable actual evapotranspiration in high-altitude areas.

A New Remote Sensing Index for Forest Dryness Monitoring Using Multi-Spectral Satellite Imagery

Canopy water content is a fundamental indicator for assessing the level of plant water stress. The correlation between changes in water content and the spectral reflectance of plant leaves at near-infrared (NIR) and short-wave infrared (SWIR) wavelengths forms the foundation for developing a new remote sensing index, the Infrared Canopy Dryness Index (ICDI), to monitor forest dryness that can be used to predict the consequences of water stress. This study introduces the index, that uses spectral reflectance analysis at near-infrared wavelengths, encapsulated by the Normalized Difference Infrared Index (NDII), in conjunction with specific canopy conditions as depicted by the widely recognized Normalized Difference Vegetation Index (NDVI). Development of the ICDI commenced with the construction of an NDII/NDVI feature space, inspired by a conceptual trapezoid model. This feature space was then parameterized, and the spatial region indicative of water stress conditions, referred to as the dry edge, was identified based on the analysis of 10,000 random observations. The ICDI was produced from the combination of the vertical distance (i.e., under consistent NDVI conditions) from an examined observation to the dry edge. Comparisons between data from drought-affected and non-drought-affected control plots in the Australian Northern Jarrah Forest affirmed that the ICDI effectively depicted forest dryness. Moreover, the index was able to detect incipient water stress several months before damage from an extended drought and heatwave. Using freely available satellite data, the index has potential for broad application in monitoring the onset of forest dryness. This will require validation of the ICDI in diverse forest systems to quantify the efficacy of the index.

Turning Night Into Day: The Creation and Validation of Synthetic Night-time Visible Imagery Using the Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) and Machine Learning

Abstract Meteorological satellite imagery is a critical asset for observing and forecasting weather phenomena. The Joint Polar Satellite System (JPSS) Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) sensor collects measurements from moonlight, airglow, and artificial lights. DNB radiances are then manipulated and scaled with a focus on digital display. DNB imagery performance is tied to the lunar cycle, with best performance during the full moon and worst with the new moon. We propose using feed-forward neural networks models to transform brightness temperatures and wavelength differences in the infrared spectrum to a pseudo lunar reflectance value based on lunar reflectance values derived from observed DNB radiances. JPSS NOAA-20 and Suomi National Polar-orbiting Partnership (SNPP) satellite data over the North Pacific Ocean at night for full moon periods from December 2018 - November 2020 were used to design the models. The pseudo lunar reflectance values are quantitatively compared to DNB lunar reflectance, providing the first-ever lunar reflectance baseline metrics. The resulting imagery product, Machine Learning Night-time Visible Imagery (ML-NVI), is qualitatively compared to DNB lunar reflectance and infrared imagery across the lunar cycle. The imagery goal is not only to improve upon the consistency performance of DNB imagery products across the lunar cycle, but ultimately lay the foundation for transitioning the algorithm to geostationary sensors, making global continuous nighttime imagery possible. ML-NVI demonstrates its ability to provide DNB derived imagery with consistent contrast and representation of clouds across the full lunar cycle for night-time cloud detection.

Constraining effects of aerosol-cloud interaction by accounting for coupling between cloud and land surface.

Aerosol-cloud interactions (ACIs) are vital for regulating Earth's climate by influencing energy and water cycles. Yet, effects of ACI bear large uncertainties, evidenced by systematic discrepancies between observed and modeled estimates. This study quantifies a major bias in ACI determinations, stemming from conventional surface or space measurements that fail to capture aerosol at the cloud level unless the cloud is coupled with land surface. We introduce an advanced approach to determine radiative forcing of ACI by accounting for cloud-surface coupling. By integrating field observations, satellite data, and model simulations, this approach reveals a drastic alteration in aerosol vertical transport and ACI effects caused by cloud coupling. In coupled regimes, aerosols enhance cloud droplet number concentration across the boundary layer more homogeneously than in decoupled conditions, under which aerosols from the free atmosphere predominantly affect cloud properties, leading to marked cooling effects. Our findings spotlight cloud-surface coupling as a key factor for ACI quantification, hinting at potential underassessments in traditional estimates.

Using automated machine learning for the upscaling of gross primary productivity

Abstract. Estimating gross primary productivity (GPP) over space and time is fundamental for understanding the response of the terrestrial biosphere to climate change. Eddy covariance flux towers provide in situ estimates of GPP at the ecosystem scale, but their sparse geographical distribution limits larger-scale inference. Machine learning (ML) techniques have been used to address this problem by extrapolating local GPP measurements over space using satellite remote sensing data. However, the accuracy of the regression model can be affected by uncertainties introduced by model selection, parameterization, and choice of explanatory features, among others. Recent advances in automated ML (AutoML) provide a novel automated way to select and synthesize different ML models. In this work, we explore the potential of AutoML by training three major AutoML frameworks on eddy covariance measurements of GPP at 243 globally distributed sites. We compared their ability to predict GPP and its spatial and temporal variability based on different sets of remote sensing explanatory variables. Explanatory variables from only Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and photosynthetically active radiation explained over 70 % of the monthly variability in GPP, while satellite-derived proxies for canopy structure, photosynthetic activity, environmental stressors, and meteorological variables from reanalysis (ERA5-Land) further improved the frameworks' predictive ability. We found that the AutoML framework Auto-sklearn consistently outperformed other AutoML frameworks as well as a classical random forest regressor in predicting GPP but with small performance differences, reaching an r2 of up to 0.75. We deployed the best-performing framework to generate global wall-to-wall maps highlighting GPP patterns in good agreement with satellite-derived reference data. This research benchmarks the application of AutoML in GPP estimation and assesses its potential and limitations in quantifying global photosynthetic activity.

Decline in Ice Coverage and Ice-Free Period Extension in the Kara and Laptev Seas during 1979–2022

The duration of ice-free periods in different parts of the Arctic Ocean plays a great role in processes in the climate system and defines the most comfortable sea ice conditions for economic activity. Based on satellite-derived sea ice concentration data acquired by passive microwave instruments, we identified the spatial distribution of the dates of sea ice retreat (DOR), dates of sea ice advance (DOA), and the resulting ice-free period duration (IFP) between these days for the Kara and Laptev seas during 1979–2022. The monthly decline in sea ice extent was detected from June to October in both seas, i.e., during the whole ice-free period. The annual mean sea ice extent during 2011–2021 decreased by 19.0% and 12.8% relative to the long-term average during 1981–2010 in the Kara and Laptev seas, respectively. The statistically significant (95% confidence level) positive IFP trends were detected for the majority of areas of the Kara and Laptev seas. Averaged IFP trends were estimated equal to +20.2 day/decade and +16.2 day/decade, respectively. The observed DOR tendency to earlier sea ice melting plays a greater role in the total IFP extension, as compared to later sea ice formation related to the DOA tendency. We reveal that regions of inflow of warm Atlantic waters to the Kara Sea demonstrate the largest long-term trends in DOA, DOR, and IFP associated with the decrease in ice coverage, that highlights the process of atlantification. Also, the Great Siberian Polynya in the Laptev Sea is the area of the largest long-term decreasing trend in DOR.

Twenty-Year Climatology of Solar UV and PAR in Cyprus: Integrating Satellite Earth Observations with Radiative Transfer Modeling

In this study, we present comprehensive climatologies of effective ultraviolet (UV) quantities and photosynthetically active radiation (PAR) over Cyprus for the period 2004 to 2023, leveraging the synergy of earth observation (EO) data and radiative transfer model simulations. The EO dataset, encompassing satellite and reanalysis data for aerosols, total ozone column, and water vapor, alongside cloud modification factors, captures the nuanced dynamics of Cyprus’s atmospheric conditions. With a temporal resolution of 15 min and a spatial of 0.05° × 0.05°, these climatologies undergo rigorous validation against established satellite datasets and are further evaluated through comparisons with ground-based global horizontal irradiance measurements provided by the Meteorological Office of Cyprus. This dual-method validation approach not only underscores the models’ accuracy but also highlights its proficiency in capturing intra-daily cloud coverage variations. Our analysis extends to investigating the long-term trends of these solar radiation quantities, examining their interplay with changes in cloud attenuation, aerosol optical depth (AOD), and total ozone column (TOC). Significant decreasing trends in the noon ultraviolet index (UVI), ranging from −2 to −4% per decade, have been found in autumn, especially marked in the island’s northeastern part, mainly originating from the (significant) positive trends in TOC. The significant decreasing trends in TOC, of −2 to −3% per decade, which were found in spring, do not result in correspondingly significant positive trends in the noon UVI since variations in cloudiness and aerosols also have a strong impact on the UVI in this season. The seasonal trends in the day light integral (DLI) were generally not significant. These insights provide a valuable foundation for further studies aimed at developing public health strategies and enhancing agricultural productivity, highlighting the critical importance of accurate and high-resolution climatological data.

SatelliteChangeNet: Deep Learning approach for Detection & Prediction

In geoscience, Detection is a useful method for analyzing land surface changes using data from Earth observation and for uncovering links between human activities and environmental phenomena. Detection in remote sensing is a rapidly evolving area of interest that is relevant for a number of fields. Recent years have seen a large number of publications and progress, even though the challenge is far from solved. This review focuses on deep learning applied to the task of Detection in multispectral remote-sensing images. In this work, SatelliteChangeNet addresses the growing need for an accurate and effective method to monitor and predict changes in satellite imagery, which is important for many purposes such as environmental monitoring, urban planning, and agriculture and disaster management. The changes always aim to show the struggle with the body and its different structures and lead to the search for a deep learning process. The program focuses on the use of satellite data for exploration of new areas, urban development analysis, environmental management damage, and change and prediction with important applications in agriculture. Water resources and farmland provide a lot of information about our planet. Analyzing these changes over time is important for understanding land use, environmental change, and natural hazards