Satellite Data Research Articles

Geological context and significance of the clay-sulfate transition region in Mount Sharp, Gale crater, Mars: An integrated assessment based on orbiter and rover data

Geological context and significance of the clay-sulfate transition region in Mount Sharp, Gale crater, Mars: An integrated assessment based on orbiter and rover data

Mechanical and Thermal Forcing for Upslope Flows and Cumulus Convection over the Sierras de Córdoba

Abstract The upslope flow processes affecting the vertical extent of orographic cumulus convection are examined using observations from the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) Field Campaign. Specifically, clear-air returns from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) C-band radar (CSAPR2) are used to characterize the structure and variability of the ridge-normal (i.e., up/downslope) flow components, which transport mass to the crest of Argentina’s Sierras de Córdoba and contribute to convective initiation. Data are compiled for the entire CACTI period (Oct-Apr), including days with clear skies, shallow cumuli, cumulus congestus, and deep convection. To examine shared variability amongst >70,000 radar scans we use (a) a principal component analysis (PCA) to isolate modes of variability in the upslope flow, and (b) composite analysis based on convective outcomes, determined from GOES16 satellite observations. These data are contextualized with observed surface sensible heat fluxes, thermodynamic profiles, and synoptic-scale analysis. Results indicate distinct thermally and mechanically forced upslope flow modes, modulated by diurnal heating and synoptic-scale variations, respectively. In some instances, there is a superposition of thermal and mechanical forcing, yielding either deeper or shallower upslope flow. The composite analyses based on satellite data show that successively deeper convective outcomes are associated with successively deeper upslope flow layers that more readily transport mass to the ridge crest in conjunction with lower lifting condensation levels, facilitating convective initiation. These results help to isolate the forcing mechanisms for orographic convection, and thus provide a foundation for parameterizing orographic convective processes in coarse resolution models.

Harnessing rooftop solar photovoltaic potential in Islamabad, Pakistan: A remote sensing and deep learning approach

Solar energy shines as a beacon for sustainable development, with rooftop solar photovoltaic (PV) installations playing a crucial role. This study proposes a novel framework to precisely assess citywide existing solar power generation and analyze future potential under various rooftop utilization scenarios (10–50 %). To illustrate the methodology, the existing solar PV yield and future potential of Islamabad are explored as a case study. Employing open-source satellite data and deep learning (DL), this study quantified the electricity generation from current solar infrastructure and projected future possibilities. DL models like Mask R–CNN and Deeplab-v3 are trained on extensive custom datasets for solar panels and rooftops extraction. The study delineated 19,000 solar arrays, covering an area of 0.8 sqkm, and extracted 75.08 sqkm of suitable rooftops for future installations. The analysis revealed, current solar infrastructure generates 141.42 GWh electricity, satisfying 6.34 % of Islamabad's annual energy demand. Utilizing 50 % rooftop area could generate 6578 GWh annually, meeting 294 % of the city's electricity needs. This research contributes to the strategic planning for solar energy infrastructure, demonstrating the substantial role rooftop solar can play in meeting urban energy needs. This can inform policy decisions and guide investment opportunities for expanding clean energy in the city.

A Review and Evaluation of the State of Art in Image-Based Solar Energy Forecasting: The Methodology and Technology Used

The increasing penetration of solar energy into the grid has led to management difficulties that require high accuracy forecasting systems. New techniques and approaches are emerging worldwide every year to improve the accuracy of solar power forecasting models and reduce uncertainty in predictions. This article aims to evaluate and compare various solar power forecasting methods based on their characteristics and performance using imagery. To achieve this goal, this article presents an updated analysis of diverse research, which is classified in terms of the technologies and methodologies applied. This analysis distinguishes studies that use ground-based sensor measurements, satellite data processing, or all-sky camera images, as well as statistical regression approaches, artificial intelligence, numerical models, image processing, or a combination of these technologies and methods. Key findings include the superior accuracy of hybrid models that integrate multiple data sources and methodologies, and the promising potential of all-sky camera systems for very short-term forecasting due to their ability to capture rapid changes in cloud cover. Additionally, the evaluation of different error metrics highlights the importance of selecting appropriate benchmarks, such as the smart persistence model, to enhance forecast reliability. This review underscores the need for continued innovation and integration of advanced technologies to meet the challenges of solar energy forecasting.

A Review and Evaluation of the State of Art in Image-Based Solar Energy Forecasting: The Methodology and Technology Used

The increasing penetration of solar energy into the grid has led to management difficulties that require high accuracy forecasting systems. New techniques and approaches are emerging worldwide every year to improve the accuracy of solar power forecasting models and reduce uncertainty in predictions. This article aims to evaluate and compare various solar power forecasting methods based on their characteristics and performance using imagery. To achieve this goal, this article presents an updated analysis of diverse research, which is classified in terms of the technologies and methodologies applied. This analysis distinguishes studies that use ground-based sensor measurements, satellite data processing, or all-sky camera images, as well as statistical regression approaches, artificial intelligence, numerical models, image processing, or a combination of these technologies and methods. Key findings include the superior accuracy of hybrid models that integrate multiple data sources and methodologies, and the promising potential of all-sky camera systems for very short-term forecasting due to their ability to capture rapid changes in cloud cover. Additionally, the evaluation of different error metrics highlights the importance of selecting appropriate benchmarks, such as the smart persistence model, to enhance forecast reliability. This review underscores the need for continued innovation and integration of advanced technologies to meet the challenges of solar energy forecasting.

Inversion Method for Chlorophyll-a Concentration in High-Salinity Water Based on Hyperspectral Remote Sensing Data.

As one of the important lakes in the "One Lake and Two Seas" of the Inner Mongolia Autonomous Region, the monitoring of water quality in Lake Daihai has attracted increasing attention, and the concentration of chlorophyll-a directly affects the water quality, making the monitoring of chlorophyll-a concentration in Lake Daihai particularly crucial. Traditional methods of monitoring chlorophyll-a concentration are not only inefficient but also require significant human and material resources. Remote sensing technology has the advantages of wide coverage and short update cycles. For lakes such as Daihai with a high salinity content, salinity is considered a key factor when inverting the concentration of chlorophyll-a. In this study, machine learning models, including model stacking from ensemble learning, a ridge regression model, and a random forest model, were constructed. After comparing the training accuracy of the three models on Zhuhai-1 satellite data, the random forest model, which had the highest accuracy, was selected as the final training model. By comparing the accuracy changes before and after adding salinity factors to the random forest model, a high-precision model for inverting chlorophyll-a concentration in hypersaline lakes was obtained. The research results show that, without considering the salinity factor, the root mean square error (RMSE) of the model was 0.056, and the coefficient of determination (R2) was 0.64, indicating moderate model performance. After adding the salinity factor, the model accuracy significantly improved: the RMSE decreased to 0.047, and the R2 increased to 0.92. This study provides a solid basis for the application of remote sensing technology in hypersaline aquatic environments, confirming the importance of considering salinity when estimating chlorophyll-a concentration in hypersaline waters. This research helps us gain a deeper understanding of the water quality and ecosystem evolution in Daihai Lake.

Spatial segregation patterns and association with built environment features in Colombian cities

Spatial segregation is a pressing issue in Latin American cities due to high urbanization, population density, and inequalities. This study examines segregation patterns in 84 middle to large cities in Colombia and their relationship with the spatial configuration of cities using satellite data. The analysis focuses on evenness and isolation metrics of segregation for ethnicity, education level, and employment status. The findings reveal higher segregation in education level and race/ethnicity in terms of evenness dimension, and in education level and employment status in terms of isolation dimension. The study also identifies associations between segregation and built environment metrics. Negative associations are observed between education level and race/ethnicity segregation and fragmentation, as well as between employment status and patches of isolation. Positive associations exist between education level and fragmentation, race/ethnicity and patches of isolation, and a negative association between employment status and street density. These results have implications for public urban policies, particularly in small and medium cities, where there is limited understanding of segregation dynamics. The study highlights the need to consider factors beyond income, such as ethnicity, in addressing spatial segregation in urban planning and policy-making.

Lake and Atmospheric Heatwaves Caused by Extreme Dust Intrusion in Freshwater Lake Kinneret in the Eastern Mediterranean

The role of dust intrusions in the formation of lake heatwaves has not yet been discussed in previous publications. We investigated a lake heatwave (LHW) and an atmospheric heatwave (AHW) in the freshwater Lake Kinneret in the Eastern Mediterranean: these were caused by an extreme dust intrusion that lasted for a 10-day period (7–17 September 2015). The AHW and LHW were defined as periods of abnormally high air temperature (Tair) and lake surface water temperature (SWT) compared to their 90th percentile thresholds in September. In the daytime, the maximal intensities of AHW and LHW reached 3 °C and 2 °C, respectively. This was despite the pronounced drop in solar radiation due to the dust radiative effect. The satellite SWT retrievals were incapable of representing the abnormally high SWT in the presence of the extreme dust intrusion. Both METEOSAT and MODIS-Terra showed a sharp decrease in the SWT compared to the actual SWT: up to 10 °C in the daytime and up to 15 °C in the nighttime. Such a significant underestimation of the actual SWT in the presence of a dust intrusion should be considered when using satellite data to analyze heatwaves. In the absence of moisture advection, the AHW and LHW were accompanied by an increase of up to 30% in absolute humidity (ρv) over the lake. Being a powerful greenhouse gas, water vapor (characterized by an increased ρv) absorbed most of both the upwelling and downwelling longwave thermal radiation, heating the near-ground atmospheric layer (which is in direct contact with the lake water surface), in the daytime and nighttime. In the nighttime, the maximal intensity of the AHW and LHW reached 4 °C and 3 °C, respectively. Because of the observed steadily increasing dust pollution over the Eastern Mediterranean during the past several decades, we anticipate that dust-related lake heatwaves will intensify adverse effects on aquatic ecosystems such as reducing fishery resources and increasing harmful cyanobacteria blooms.

Atmospheric and Radiometric Normalization of Satellite Images for Landscape-Level Environmental Monitoring: The Case of The Mediterranean Region

Ensuring atmospheric and radiometric consistency among the frameworks of satellite data used in regional studies is a critical requirement for change detection studies employed in regional planning monitoring. The purpose of this article is to provide a guide for the necessary atmospheric correction and radiometric normalization processes required in generating environmental data at the landscape level for physical planning. In this context, adjustments were made to remove atmospheric effects before merging multiple ASTER satellite image frames used in a project supported by TÜBİTAK, covering landscape-level environmental inventory and monitoring. The Dark Object Subtraction method with the Cos(t) model was utilized in the atmospheric correction process. Subsequently, separate regression relationships were computed for each band by considering overlapping areas on adjacent tracks of ASTER data, and radiometric normalization was performed based on these regression equations. Thus, differences between satellite images used in monitoring land changes and affecting multiple frames were minimized.

Мониторинг границ биомов суши в глобальном масштабе по данным спутниковых наблюдений

Global climate change leads to the transformation and imbalance of all natural systems, to a change in the boundaries of biomes. In this regard, it became necessary to monitor the boundaries of land biomes and to search for methods for determining the boundaries of biomes, ecotones and their control. The aim of this work is to monitor the boundaries of biomes, and to study the seasonal dynamics of NDVI dispersion. The methodological basis of the study is satellite observations and the NDVI moving dispersion method. The dynamics of the moving dispersion of NDVI biomes and ecotones of the land from 2000 to 2020 is estimated in the work. As a result of research, a raster map of the spatial distribution of NDVI dispersions for various biomes (plant communities) was constructed. The raster map of the dispersion distribution of NDVI vegetation is based on MODIS satellite data (product MCD12C1). It is shown that dispersions of NDVI biomes (zones with a certain type of vegetation) have low values. In the transition zones between biomes in ecotones, the dispersion of NDVI increases significantly. The raster map was compared with a biome vector map. The coincidence of biomes on vector biological maps and raster maps showing dispersions of NDVI was revealed. The main factors determining the boundaries of biomes are the spatial and temporal distribution of temperatures, the greatest changes of which occur in the south of the tundra zone and in the north-boreal forests. Studies of the dynamics of biotemperature in the tundra for the period 2001–2020 years showed a positive trend. The results of the spatial distribution of NDVI dispersions over the seasons of the year (winter, spring, summer, and autumn) give an idea of the dynamics of phenological processes on a global scale.

A scalable big data approach for remotely tracking rangeland conditions

Rangelands, covering half of the global land area, are critically degraded by unsustainable use and climate change. Despite their extensive presence, global assessments of rangeland condition and sustainability are limited. Here we introduce a novel analytical approach that combines satellite big data and statistical modeling to quantify the likelihood of changes in rangeland conditions. These probabilities are then used to assess the effectiveness of management interventions targeting rangeland sustainability. This approach holds global potential, as demonstrated in Mongolia, where the shift to a capitalist economy has led to increased livestock numbers and grazing intensity. From 1986 to 2020, heavy grazing caused a marked decline in Mongolia’s rangeland condition. Our evaluation of diverse management strategies, corroborated by local ground observations, further substantiates our approach. Leveraging globally available yet locally detailed satellite data, our proposed condition tracking approach provides a rapid, cost-effective tool for sustainable rangeland management.

Increasing frequency and intensity of the most extreme wildfires on Earth.

Climate change is exacerbating wildfire conditions, but evidence is lacking for global trends in extreme fire activity itself. Here we identify energetically extreme wildfire events by calculating daily clusters of summed fire radiative power using 21 years of satellite data, revealing that the frequency of extreme events (≥99.99th percentile) increased by 2.2-fold from 2003 to 2023, with the last 7 years including the 6 most extreme. Although the total area burned on Earth may be declining, our study highlights that fire behaviour is worsening in several regions-particularly the boreal and temperate conifer biomes-with substantial implications for carbon storage and human exposure to wildfire disasters.

Discrimination of Fe-Ni-Laterites from Bauxites Using a Novel Support Vector Machines-Based Methodology on Sentinel-2 Data

Currently, the global shift towards green energy is at the forefront of efforts introducing a new era, thus rendering exploration for critical raw materials essential. To this purpose, the utilization of advanced machine learning methods in remote sensing has emerged as a rapid and cost-effective approach. This study proposes a new methodology, utilizing Sentinel-2 satellite data, to distinguish ferronickel (Fe-Ni-) laterite from bauxite across pre-mining, mining, and post-mining occurrences worldwide. Both ores contain mineral raw materials such as nickel, iron, cobalt, and alumina and their discrimination is generally macroscopically challenging, especially when their locations are often in geographical proximity. The proposed method is based on Support Vector Machines (SVM) classification using spectral signatures of known Fe-Ni-laterite and bauxite-bearing pixels in Greece, Cuba, and Jamaica. The highest classification accuracies are obtained by combining 𝑏12 with 𝑏6 or 𝑏7 spectral bands. Comparisons with specific ore mineralogies show that 𝑏6 and 𝑏7 are strongly linked to the ferric phase, while 𝑏12 is mainly associated with the argillic mineralogies, the latter probably being the key discriminating factor between the two ores. From laboratory chemical analyses, we also establish that 𝑏12 and 𝑏6 or 𝑏7 are strongly associated with Al2O3 and Fe2O3 content correspondingly. The proposed method is accurate, it has reduced prospection costs, and it can facilitate the initial screening of broad areas by automatically characterizing whether an ore is bauxite or Fe-Ni-laterite. This underscores the methodology’s significance in ore differentiation and exploration within the context of green energy endeavors.

Satellite data is revealing long time changes in the world largest lakes

Lakes are a crucial source of drinking water, provide ecological services from fisheries and aquaculture to tourism and are also a critical part of the global carbon cycle. Therefore, it is important to understand how lakes are changing over time. The ESA Ocean Colour Climate Change Initiative (OC-CCI) database allows to study changes in the largest lakes over 1997–2023 period. The Caspian Sea and ten next largest lakes were under investigation. Changes in the phytoplankton biomass (Chl-a), the concentration of particulate matter (bbp(555)), the colored dissolved organic matter, CDOM (adg(412)), and the light diffuse attenuation coefficient in water (Kd(490)) were analyzed. Both increasing and decreasing trends (or no significant trend at all) of studied parameters were observed in these lakes over the study period. In some of the Laurentian Great Lakes the changes in CDOM over the study period were found to be in accordance with the lake water level changes i.e. with the inflow from the catchment. There was difference between the trends of Chl-a and bbp(555) in lakes Michigan and Huron indicating that there may have been shift in phytoplankton community that took place around 2005. The study demonstrated that remote sensing products, like the ones created by ESA OC-CCI, are valuable tools to study behavior of large lakes ecosystems over time.

Examining chlorophyll-a concentrations in tropical reservoirs under various land use changes using Sentinel – 2 and Google Earth engine – Bhadra and Tungabhadra, India

The understanding of spatio-temporal variation in land use and land cover (LULC) patterns is crucial for managing catchment land use planning, as it directly influences of tropical reservoir water quality and the subsequent Nutrient Contamination (NC) of unmonitored water bodies. The current research attempts to accurately measure the influence of LULC and its associated determinants on the quantities of NC loads by using Chl-a as a proxy, within tropical reservoirs, i.e. Bhadra and Tungabhadra, located in same river catchment. This Chl-a spread calculated by the Maximum Chlorophyll Index (MCI) derived from Sentinel 2 satellite data products covering the period from July 2016 to June 2021 were done using Google Earth Engine (GEE) platform. The validation analysis confirms the robustness of the methodology with a strong correlation between MCI-calculated values and EOMAP (Earth Observation and Environmental Services Mapping) Chl-a (μg/L) data points for both reservoirs, Bhadra (R2 = 0.64) and Tungabhadra (R2 = 0.68). The findings reveal that, Tungabhadra reservoir consistently exhibits an excessive spatial distribution of Chl-a spread area (17 km2 to 335 km2), reflecting nutrient-rich water inflows, particularly evident during the post-monsoon period. This notable rise could be linked to harvesting the Kharif crop, resulting in elevated nutrient concentrations. In contrast Bhadra reservoir, dominated by forested areas, maintains relatively lower Chl-a spread areas (<20 km2), highlighting its pivotal role in maintaining water cleanliness and serves as a riparian boundary. In addition, the changes in LULC classes show a strong relationship with variation in Chl-a during the studied period, for the Bhadra Reservoir R2 = 0.51 (F- statistics = 3.983, p = 0.021), and the Tungabhadra Reservoir R2 = 0.802 (F- statistics = 7.489, p = 0.0143). This highlights how changes in land use significantly shape contamination dynamics, deepening our understanding of nutrient inputs and contamination drivers in tropical reservoirs.

Многолетняя урбанизация территории Республики Бенин на примере города Котону

Good management of a country`s resources is essential to its development. Whether it is land, agricultural or humans, each element plays an important role. The Republic of Benin is situated in West Africa; its economic potential is great. Indeed, for almost 8 years, the country has made great progress in terms of industrialization, road infrastructure and land security having improved each sector thanks to the new reforms started by its government. The authors present the results of a study on the development of Cotonou, one of the most important cities in the country. Actually, it is its economic capital. We used satellite, historical and land data to show the effects of uncontrolled urbanization over a period of 30 years, the consequences of poor soil study as well as the importance of effective management of land resources for the proper development of the territory

Determination of Mountain Equivalent Rainstorm (MER) in Qinba Maintain Area Based on TRMM

The study of extreme precipitation is a significant aspect for investigating rainstorms, flash floods, and unpredictable disasters. Qinba mountain, Shaanxi province, China, is sensitive to extreme climate and rainstorm events. It is crucial to investigate the feature of precipitation extremes in this region with satellite data. According to this, the paper using the 1Day extreme precipitation datasets of TRMM and rain-gauge to calculate the mountain rainstorm, then the statistical metrics (CC, MBE, RMSE) was used in validation as the performance measure. The 1Day, 3Day, 5Day, and 7Day extreme precipitation was identified by the 95&amp;lt;sup&amp;gt;th&amp;lt;/sup&amp;gt; percentile method. Thus to determine the Mountain Equivalent Rainstorm (MER). As the results, (1) Based on the comparison, the TRMM satellite product can capture the extreme precipitation mostly at the station below 433m (R&amp;lt;sup&amp;gt;2 &amp;lt;/sup&amp;gt;&amp;gt;0.5) for 5Day datasets, while 7Day datasets reveal contrast patterns. (2) By applying the MER concept, the TRMM-based and gauge-based ratio revealed a similar pattern of mountain rainstorms at higher elevations and slightly different in the middle region. The mountain rainstorm amount was double the extreme rainfall at a higher elevation. Therefore, the defined extreme precipitation characteristics can assist the disaster risk reduction and mitigation strategy in the Qinba mountain of Shaanxi Province, China, and also provide a reference for improving the satellite algorithm in extreme precipitation measurement.

Analysis of Urban Thermal Environment Improved by Blue-Green Spaces on the Landsat Data: A Case Study on Tianjin

China’s urbanization is characterized by rapid speed, large scale and high energy consumption, which causes a series of ecological problems. Among these problems, urban thermal environment is particularly prominent, and the most intuitive reflection is the heat island effect. It is of great significance to optimize the urban layout to give full play to the cooling effect of blue-green spaces for improving urban thermal environment. Compared with other methods, it is more extensive and feasible. However, the current research on urban thermal environment mostly focuses on revealing the driving effects of different land use, and lacks of in-depth discussions on blue-green spaces. Tianjin is located in the northeast of the North China Plain, and has a typical warm temperate sub-humid monsoon climate. This paper takes the six districts of Tianjin City as the research area, covering Heping District, Nankai District, Hongqiao District, Hebei District, Hexi District and Hedong District, with a total area of about 181.18 square kilometers. The typical climate and urbanization characteristics of Tianjin can provide reference for cities with the same characteristics. Firstly, this paper had adopted the Landsat changing scenario of the city gained during the periods of 2004, 2011 and 2017 to formulate the land surface temperature by Mono-window Algorithm, and divided the results into seven temperature zones by standard deviation classification method. In regard to blue-green spaces, this paper had identified Landsat satellite data by Maximum Likelihood Classifier, and classified land use into three categories: blue spaces, green spaces, and non-blue and green spaces. And, next, this paper had studied the correlation between the landscape pattern of blue-green spaces and urban thermal environment by Landscape Pattern Analysis, and used Moving-window Analysis to compare the relationship between thermal environment and blue-green spaces at different spatial scales. And so the research results prove that, the high-temperature regions of Tianjin has been expanding along with the expansion and agglomeration of the urban areas and across the river space restrictions, while the low-temperature regions of the city are mainly concentrated in the blue-green spaces, during the periods of 2004, 2011 and 2017. Furthermore, in the landscape pattern level, there is a significant negative correlation between patch perimeter index of blue-green spaces and land surface temperature, and a significant positive correlation between patch perimeter area ratio index of blue-green spaces and land surface temperature. In the multi-scale spatial pattern, the correlation between the Percent of Landscape of blue spaces patches and land surface temperature is the highest in the 1500 meters × 1500 meters sample, and the Percent of Landscape of green spaces patches and land surface temperature is the highest in the 300 meters × 300 meters sample. In addition, the improvement efficiency of thermal environment in blue spaces is higher. In the end, the optimization suggestions of adjusting the layout of blue-green spaces to effectively improve the urban thermal environment problem are proposed. By optimizing the blue-green space landscape patterns at the optimal scale, increasing the blue spaces in the northern high temperature range, widening and transforming the existing waterways, constructing riverside greenways and other strategies to effectively deal with the thermal environmental problems, improving the ecology and livability of the city. It is hoped to provide new ideas and references for the construction of ecological and livable city under the background of rapid urbanization. In the research process, due to the limitation of data sources, calculation methods and other factors, the relevant conclusion is still limited at the spatial scale. In the future research, the thermal environment and the pattern of blue-green spatial landscape at the fine scale will be studied.

Granular satellite data to assess the potential for nature based solutions at a national scale: a proof of concept with data from Rwanda and Lesotho

ABSTRACT Nature-based Solutions (NbS) form a substantial part of cost-efficient climate change mitigation options. However, public financial flows towards NbS have been limited. Among the factors impeding investments in NbS are challenges in reliable remote project identification and comparison. In this article, we demonstrate a technological solution to these challenges. Using cloud-based satellite data processing and the proliferation of open geospatial data, we developed a methodology to map NbS suitability based on a set of biophysical, pedological, hydrological and climatological criteria. To provide a proof of concept, we identify potential project areas for eight types of NbS in Rwanda and Lesotho. Building on these spatially-explicit NbS suitability data layers (accessible at https://wb-nbs.users.earthengine.app/view/ncs-potential), we develop marginal abatement cost curves for projects in Rwanda and Lesotho. We thus provide a proof of concept for remote identification and project comparison. These technological developments can help investors, such as multilateral donors or governments, alleviate additionality concerns that are implied by local project selection. Key Policy Insights Investment in climate change mitigation through Nature-based Solutions (NbS) is limited, in spite of the large, cost-efficient potential for achieving national and global goals. Remote project identification and comparison can help alleviate some of the impediments to international investment in NbS. Such remote identification and comparison of NbS projects is now possible because of breakthroughs in cloud-based satellite data processing technologies and increased availability of geospatial data. We present a proof of concept, establishing marginal abatement cost curves for climate change mitigation through NbS in Rwanda and Lesotho. Coupled with the institutional improvements necessary to create a reliable intergovernmental carbon credit market, these technological developments have the potential to boost international investment in NbS.

SATELLITE MONITORING OF DRAGOMAN MARSH FOR THE PERIOD 2018-2023

In this study, monitoring of the Dragoman Marsh was conducted for the period 2018-2023. The Dragoman Marsh is distinguished as the largest natural karst wetland area in Bulgaria. Monitoring of this natural formation is essential for studying its dynamic processes and ensuring its protection. The use of satellite data represents a key tool in this context. The data from the Sentinel 2 satellite being utilized for this purpose. Following appropriate processing of this data, classification of the object by years and seasons - spring and summer-autumn - was performed, using indices such as NDVI, NDWI, and TCT based index - NDGI. The obtained results contribute to a better understanding of the development of the wetland area and provide guidance for its conservation and management.