Subjects -> EARTH SCIENCES (Total: 771 journals)
    - EARTH SCIENCES (527 journals)
    - GEOLOGY (94 journals)
    - GEOPHYSICS (33 journals)
    - HYDROLOGY (29 journals)
    - OCEANOGRAPHY (88 journals)

EARTH SCIENCES (527 journals)            First | 1 2 3     

Showing 401 - 371 of 371 Journals sorted alphabetically
Physical Geography     Hybrid Journal   (Followers: 8)
Physical Science International Journal     Open Access  
Physics in Medicine & Biology     Full-text available via subscription   (Followers: 15)
Physics of Life Reviews     Hybrid Journal   (Followers: 1)
Physics of Metals and Metallography     Hybrid Journal   (Followers: 18)
Physics of Plasmas     Hybrid Journal   (Followers: 10)
Physics of the Earth and Planetary Interiors     Hybrid Journal   (Followers: 34)
Physics of the Solid State     Hybrid Journal   (Followers: 4)
Physics of Wave Phenomena     Hybrid Journal  
Physics World     Full-text available via subscription   (Followers: 18)
Physik in unserer Zeit     Hybrid Journal   (Followers: 9)
Pirineos     Open Access  
Planet     Open Access   (Followers: 4)
Plasma Physics and Controlled Fusion     Hybrid Journal   (Followers: 6)
Plasma Physics Reports     Hybrid Journal   (Followers: 7)
Polar Record     Hybrid Journal   (Followers: 2)
Positioning     Open Access   (Followers: 4)
Pramana     Open Access   (Followers: 13)
Precambrian Research     Hybrid Journal   (Followers: 7)
Preview     Hybrid Journal  
Proceedings of the Geologists' Association     Full-text available via subscription   (Followers: 6)
Proceedings of the Linnean Society of New South Wales     Full-text available via subscription   (Followers: 2)
Proceedings of the Yorkshire Geological Society     Hybrid Journal   (Followers: 1)
Progress in Earth and Planetary Science     Open Access   (Followers: 15)
Pure and Applied Geophysics     Hybrid Journal   (Followers: 12)
Quarterly Journal of Engineering Geology and Hydrogeology     Hybrid Journal   (Followers: 4)
Quaternary     Open Access  
Quaternary Australasia     Full-text available via subscription  
Quaternary Geochronology     Hybrid Journal   (Followers: 8)
Quaternary International     Hybrid Journal   (Followers: 14)
Quaternary Research     Full-text available via subscription   (Followers: 19)
Quaternary Science Advances     Open Access  
Quaternary Science Reviews     Hybrid Journal   (Followers: 26)
Radiocarbon     Hybrid Journal   (Followers: 12)
Remote Sensing     Open Access   (Followers: 57)
Remote Sensing Applications : Society and Environment     Full-text available via subscription   (Followers: 9)
Remote Sensing in Earth Systems Sciences     Hybrid Journal   (Followers: 5)
Remote Sensing Letters     Hybrid Journal   (Followers: 45)
Remote Sensing Science     Open Access   (Followers: 29)
Rendiconti Lincei     Hybrid Journal  
Reports on Geodesy and Geoinformatics     Open Access   (Followers: 8)
Reports on Mathematical Physics     Full-text available via subscription   (Followers: 2)
Research & Reviews : Journal of Space Science & Technology     Full-text available via subscription   (Followers: 18)
Resource Geology     Hybrid Journal   (Followers: 6)
Resources, Environment and Sustainability     Open Access   (Followers: 1)
Results in Geochemistry     Open Access  
Results in Geophysical Sciences     Open Access  
Reviews in Mineralogy and Geochemistry     Hybrid Journal   (Followers: 4)
Reviews of Modern Physics     Full-text available via subscription   (Followers: 31)
Revista Cerrados     Open Access  
Revista de Ciências Exatas Aplicadas e Tecnológicas da Universidade de Passo Fundo : CIATEC-UPF     Open Access  
Revista de Ingenieria Sismica     Open Access  
Revista de Investigaciones en Energía, Medio Ambiente y Tecnología     Open Access  
Revista de la Academia Colombiana de Ciencias Exactas, Físicas y Naturales     Open Access  
Revista de Teledetección     Open Access  
Revista Geológica de Chile     Open Access  
Revue Française de Géotechnique     Hybrid Journal  
Rock Mechanics and Rock Engineering     Hybrid Journal   (Followers: 7)
Rocks & Minerals     Hybrid Journal   (Followers: 3)
Russian Geology and Geophysics     Hybrid Journal   (Followers: 2)
Russian Journal of Mathematical Physics     Full-text available via subscription  
Russian Journal of Pacific Geology     Hybrid Journal  
Russian Physics Journal     Hybrid Journal   (Followers: 1)
Science China Earth Sciences     Hybrid Journal   (Followers: 3)
Science News     Hybrid Journal   (Followers: 11)
Science of Remote Sensing     Open Access   (Followers: 7)
Scientific Annals of Stefan cel Mare University of Suceava. Geography Series     Open Access  
Scientific Journal of Earth Science     Open Access   (Followers: 1)
Scientific Reports     Open Access   (Followers: 85)
Sedimentary Geology     Hybrid Journal   (Followers: 20)
Sedimentology     Hybrid Journal   (Followers: 15)
Seismic Instruments     Hybrid Journal   (Followers: 1)
Seismological Research Letters     Full-text available via subscription   (Followers: 12)
Soil Dynamics and Earthquake Engineering     Hybrid Journal   (Followers: 14)
Soil Security     Open Access   (Followers: 3)
Solid Earth     Open Access   (Followers: 5)
Solid Earth Discussions     Open Access   (Followers: 1)
Solid Earth Sciences     Open Access   (Followers: 1)
South African Journal of Geomatics     Open Access   (Followers: 2)
Standort - Zeitschrift für angewandte Geographie     Hybrid Journal   (Followers: 2)
Stratigraphy and Geological Correlation     Full-text available via subscription   (Followers: 2)
Studia Geophysica et Geodaetica     Hybrid Journal   (Followers: 1)
Studia Geotechnica et Mechanica     Open Access  
Studia Universitatis Babes-Bolyai, Geologia     Open Access  
Survey Review     Hybrid Journal   (Followers: 6)
Surveys in Geophysics     Hybrid Journal   (Followers: 3)
Swiss Journal of Palaeontology     Hybrid Journal   (Followers: 4)
Tectonics     Full-text available via subscription   (Followers: 15)
Tectonophysics     Hybrid Journal   (Followers: 24)
Tellus A     Open Access   (Followers: 21)
Tellus B     Open Access   (Followers: 20)
Terra Latinoamericana     Open Access  
Terra Nova     Hybrid Journal   (Followers: 5)
The Compass : Earth Science Journal of Sigma Gamma Epsilon     Open Access  
The Holocene     Hybrid Journal   (Followers: 16)
The Leading Edge     Hybrid Journal   (Followers: 1)
Transportation Infrastructure Geotechnology     Hybrid Journal   (Followers: 8)
Turkish Journal of Earth Sciences     Open Access  
UD y la Geomática     Open Access  
Unconventional Resources     Open Access  
Underwater Technology: The International Journal of the Society for Underwater     Full-text available via subscription   (Followers: 1)
Universal Journal of Geoscience     Open Access  
Unoesc & Ciência - ACET     Open Access  
Vadose Zone Journal     Open Access   (Followers: 5)
Volcanica     Open Access  
Water     Open Access   (Followers: 10)
Water International     Hybrid Journal   (Followers: 19)
Water Resources     Hybrid Journal   (Followers: 21)
Water Resources Research     Full-text available via subscription   (Followers: 94)
Watershed Ecology and the Environment     Open Access  
Weather, Climate, and Society     Hybrid Journal   (Followers: 15)
Wiley Interdisciplinary Reviews - Climate Change     Hybrid Journal   (Followers: 33)
World Environment     Open Access   (Followers: 1)
Yearbook of the Association of Pacific Coast Geographers     Full-text available via subscription   (Followers: 2)
Yugra State University Bulletin     Open Access   (Followers: 1)
Zeitschrift der Deutschen Gesellschaft für Geowissenschaften     Full-text available via subscription   (Followers: 3)
Zeitschrift für Geomorphologie     Full-text available via subscription   (Followers: 5)
Zitteliana     Open Access  
Землеустрій, кадастр і моніторинг земель     Open Access   (Followers: 1)

  First | 1 2 3     

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Journal Cover
Remote Sensing in Earth Systems Sciences
Number of Followers: 5  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2520-8195 - ISSN (Online) 2520-8209
Published by Springer-Verlag Homepage  [2469 journals]
  • Impact of Urbanization and Spatio-temporal Estimation of Land Surface
           Temperature in a Fast-growing Coastal Town in Kerala, Western Coast of
           Peninsular India

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      Abstract: Abstract The large-scale degradation of the environment has happened due to the rapid urbanization and non-scientific developments around major towns in the world. The main problem related to this scenario is the population density and fast socio-economic development in the area. The current study is an effort to investigate the spatio-temporal variations in the urban growth, land use/land cover, and the land surface temperature (LST) of a coastal town on the western coast of peninsular India that experiences a tropical climate. US-based Landsat imageries of 1988, 1997, 2001, 2014, 2016, and 2018 have been used for the investigation. Multiband analyses were performed for the estimation of geospatial indicators. The study revealed a noticeable decrease in vegetated areas and barren lands over 30 years. The built-up area increased from 1 to 22% of the total study area during these years. The average land surface temperature has been augmented from 22.2 to 29.2 °C in the study area from 1988 to 2018. Normalized difference built-up index (NDBI) showed a significant positive correlation with land surface temperature (LST) from 1988 to 2018, Whereas, normalised difference vegetation index (NDVI) recorded a significant negative correlation with LST each year. The present study revealed the need for increasing the green cover and making the developments more sustainable and environmentally friendly. The spatio-temporal database generated through the study can be used as input for future development and conservation planning and management.
      PubDate: 2022-08-09
       
  • The Assessment of Meteorological Drought Impact on the Vegetation Health
           Index

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      Abstract: Abstract One of the critical consequences of drought is the reduction of water resources and reduction of agricultural production. Therefore, it is essential to evaluate the relationship between meteorological drought and vegetation. To investigate this relationship, meteorological data from 28 rain gauge and remote sensing stations located in Lorestan Province and its neighboring regions were used in this study. First, the standardized precipitation index (SPI) was calculated between 1987 and 2017 using meteorological data, and then, the vegetation health index (VHI) was calculated using satellite images for the same years. The correlation between SPI and VHI was computed by Pearson’s correlation coefficient. The results showed that the highest Pearson’s correlation coefficient was 0.77, belonging to the SPIs for October and November with 9- and 12-month time periods. Multivariate linear regression was also performed between the SPI and vegetation health index (VHI), and the results showed that SPI was significantly correlated with VHI at a 5% level over 9- and 12-month periods. Finally, a confusion matrix was used to evaluate the compatibility of the SPI and VHI drought classes.
      PubDate: 2022-07-25
      DOI: 10.1007/s41976-022-00074-5
       
  • Using Multi-decadal Satellite Records to Identify Environmental Drivers of
           Fire Severity Across Vegetation Types

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      Abstract: Abstract To date, most studies of fire severity, which is the ecological damage produced by a fire across all vegetation layers in an ecosystem, using remote sensing have focused on wildfires and forests, with less attention given to prescribed burns and treeless vegetation. Our research analyses a multi-decadal satellite record of fire severity in wildfires and prescribed burns, across forested and treeless vegetation, in western Tasmania, a wet region of frequent clouds. We used Landsat satellite images, fire history mapping and environmental predictor variables to understand what drives fire severity. Remotely-sensed fire severity was estimated by the Delta Normalised Burn Ratio (ΔNBR) for 57 wildfires and 70 prescribed burns spanning 25 years. Then, we used Random Forests to identify important predictors of fire severity, followed by generalised additive mixed models to test the statistical association between the predictors and fire severity. In the Random Forests analyses, mean summer precipitation, mean minimum monthly soil moisture and time since previous fire were important predictors in both forested and treeless vegetation, whereas mean annual precipitation was important in forests and temperature seasonality was important in treeless vegetation. Modelled ΔNBR (predicted ΔNBRs from the best-performing generalised additive mixed model) of wildfire forests was higher than modelled ΔNBR of prescribed burns. This study confirms that western Tasmania is a valuable pyrogeographical model for studying fire severity of wet ecosystems under climate change, and provides a framework to better understand the interactions between climate, fire severity and prescribed burning.
      PubDate: 2022-07-07
      DOI: 10.1007/s41976-022-00070-9
       
  • Change Detection and Feature Extraction Using High-Resolution Remote
           Sensing Images

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      Abstract: Abstract Change detection using high temporal resolution remote sensing satellite data for identifying changes on the Earth’s surface is critical in urban applications, including vacant land site monitoring. Physical ground surveys, for monitoring the vacant site, are a time-consuming process. Results of analysis of satellite data for identifying changes vary, based on the image interpretation skills and satellite data resolution. The application of computer vision tools and libraries for change detection using image interpretation has shown some excellent results. It can be further enhanced by adding machine learning techniques. This study focuses on integration of binary change detection with machine learning techniques for identifying the change detection and for monitoring the vacant sites in an urban area. Edge detection technique coupled with principal component analysis and k-means clustering for generating change map successfully depicts the changes. Change detection results are further enhanced by adding feature type information derived using machine learning–based classifiers. Random forest classifiers are used to classify and identify different land use classes within the urban area: water bodies, cropland, built-up, roads, and bare land. The approach is evaluated on different areas, giving an overall accuracy of 88.2%, precision of 84.8%, and an F1 score of 81.6% for classification. The classification results are integrated with change detection results to identify changes where the bare land is transformed into built-up by identifying buildings/houses. The work will be helpful in urban planning bodies having multiple vacant land sites for monitoring.
      PubDate: 2022-06-17
      DOI: 10.1007/s41976-022-00073-6
       
  • Evaluation of Nonparametric Machine-Learning Algorithms for an Optimal
           Crop Classification Using Big Data Reduction Strategy

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      Abstract: Accurate crop classification can support analyses of food security, environmental, and climate changes. Most of the current research studies have focused on applying available algorithms to classify dominant crops on the landscape using one source of remotely sensed data due to geoprocessing constraints (e.g., big data access, availability, and processing power). In this research, we compared four classification algorithms, including the support vector machine (SVM), random forest (RF), regression tree (CART), and backpropagation network (BPN), to select a robust and efficient classification algorithm able to classify accurately many crop types. We used multiple sources of satellite images such as Sentinel-1 (S1) and Sentinel-2 (S2) and developed a new cropping classification method for a study site in the Bekaa valley, Lebanon, fully implemented on Google Earth Engine Platform, which minimized those geoprocessing constraints. The algorithm selection was based on their popularity, availability, simplicity, similarity, and diversity. In addition, we adopted different strategies that included changing the number of crops. The first strategy is to reduce the number of collected S2 images thereafter S1; the second strategy is to use S2 images separately and then combining S2 and S1. This study results proved that the RF is the most robust algorithm for crop classification, showing the highest overall accuracy (OA) (95.4%) and a kappa index of 0.94, followed by BPN, SVM, and CART, respectively. The performance of these algorithms based on major crop types such as wheat or potato showed that CART is the highest with OA (98%) followed by RF, SVM, and BPN, respectively. Nevertheless, CART fails to classify other minor crop types. We concluded that RF is the best algorithm for classifying different crop types in the study area, using multiple remote sensing data sources. Graphical abstract
      PubDate: 2022-06-17
      DOI: 10.1007/s41976-022-00072-7
       
  • Assessment of Different Spectral Unmixing Techniques on Space Borne
           Hyperspectral Imagery

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      Abstract: Abstract   Spectral unmixing decomposes the mixed pixels into constituent land cover features present in that pixel. This can be understood through the concepts of affine, convex and projective geometries. Spectral unmixing is difficult to implement in coarser spatial resolution space-borne hyperspectral data, due to the natural heterogeneity of the different land cover features. Linear spectral unmixing (LSU) follows linear equations for generating fractional coefficients; however, it contains limitations like its inability to handle noisy pixels, least-square error calculation, etc. Mixture tuned matched filtering (MTMF) is a partial unmixing technique in which user-defined targets are mapped. This approach uses a matched filter (MF) and linear mixture theory in combination. Whereas simplex projection unmixing (SPU) technique is nonlinear and is utilized for resolving problems such as fully constrained least square and projecting a point onto a simplex. In this study, Hyperion data was used for performing spectral unmixing using LSU, MTMF, and SPU techniques. The unmixing results obtained were compared and validated using available images from geo-portals. The abundance images of SPU were observed better than MTMF and LSU in terms of the material identification. The variation in the percentage aerial coverage of the land cover features in the mixed pixel is found closer in the abundance results of SPU, i.e., 0.1–3.4% whereas MTMF and LSU have a variation of 0.6–5.2% and 1.9–8.7%, respectively. Rule-based classification was performed on the “abundance images” and SPU classification outperformed the other two techniques, as it enabled differentiation of most of the land cover features.
      PubDate: 2022-06-10
      DOI: 10.1007/s41976-022-00071-8
       
  • Evaluation of Monthly Precipitation Data from Three Gridded Climate Data
           Products over Nigeria

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      Abstract: Abstract The use of satellite and reanalysis weather product is gaining traction in the scientific community especially in developing worlds where in situ data are sparse. Tropical locations have dynamic and widely variable climate which needs to be continuously monitored. The efficiency of these products in capturing the climatic dynamics of these regions is important. The aim of this research is to investigate the performance of three gridded precipitation products (the University of Delaware (UDEL), NOAA’s precipitation reconstruction over land (NOAA), and Global Precipitation Climatology Centre (GPCC)) across 21 locations within tropical Nigeria. The performance of the gridded data was assessed with gauge data from the Nigerian Meteorological Services (NIMET) over a period of 51 years (1960–2010). Correlation values in the range 0.68–0.92, 0.69–0.92 and 0.30–0.93 were obtained for GPCC, NOAA, and UDEL respectively in all stations. The three products have poor performance in the northern stations of the country during the dry season but good performance in all stations during the wet season. The GPCC gridded product was found to have the best performance over the region.
      PubDate: 2022-06-07
      DOI: 10.1007/s41976-022-00069-2
       
  • Development of a Raspberry Pi–Based Remote Station Prototype for Coastal
           Environment Monitoring

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      Abstract: Abstract Monitoring of the marine and coastal environment using standard measuring equipment is not without incurring a significant amount of cost. This study was geared at prospecting relatively inexpensive environmental monitoring instrument using the Raspberry Pi computer in combination with commonly available sensors. Atmospheric temperature, humidity, and sea surface temperature (SST) were monitored using locally assembled low-cost measuring equipment with a subsequent comparison with data from a standard weather station. The developed instrument was consequently evaluated for its efficacy and various functionalities in coastal environmental monitoring. DHT11 and DHT22 sensors are relatively cheap and both measure atmospheric temperature and humidity, while a DS19B20 waterproof digital thermometer measures water temperature. These sensors were incorporated in a locally built in situ measuring equipment interfaced by a Python-programmed Raspberry Pi for acquiring data. A successful assemblage and deployment of the device in a near-shore coastal marine environment yielded efficient and accurate data recorded by the DHT22 and DS19B20 sensors. A comparison of the DS18B20-measured SST to SST from Sentinel-3 satellite revealed no significant difference for a simple T-test and with R2 and root mean square error (RMSE) values of 0.172 and 2.15 °C respectively. Similarly, a comparison of atmospheric temperature and humidity between the developed equipment using DHT22 sensor, and the standard weather station yielded strong positive correlations (0.92 and 0.93) and with R2 of 0.71 and 0.58, and RMSE of 0.92 °C and 3.1% respectively. A transformation of the data from the developed equipment with respective regression equations yielded further significant improvements in the results with R2 values of 0.93, 0.84 and 0.87, and RMSE values of 0.63 °C, 0.68 °C and 1.74% respectively for SST (DS19B20), atmospheric temperature (DHT22) and humidity (DHT22). Although the DHT11 sensor recorded higher errors in atmospheric temperature and humidity data due to its low operating tolerance ranges, an application of respective regression equations also yielded improved results. This study has successfully demonstrated the potential of developing and using locally assembled relatively low-cost equipment for environmental monitoring where funding is a constraint for small-scale research and operational in situ observations.
      PubDate: 2022-06-01
      DOI: 10.1007/s41976-021-00053-2
       
  • Enhancing Satellite Oceanography-Driven Research in West Africa: a Case
           Study of Capacity Development in an Underserved Region

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      Abstract: Abstract Marine business and resources play a major role in the economics and way of life in coastal West African countries. Such countries see great profitability from their marine resources while also facing challenges that come with a bordering sea. Despite this fact, there has been limited research into the optimal way for West African Coastal States to coexist with, and sustainably use their marine resources, a research deficit that is mainly due to a lack of infrastructure for in-situ work, lack of capacity development, and comprehensive datasets to undertake oceanographic research. The Coastal Ocean Environment Summer School in Ghana (COESSING; www.coessing.org) was developed to help meet some of these challenges. Each summer since 2015, ocean scientists (e.g., biologists, chemists, physicists, hydrologists) from the USA and Europe have collaborated with West African colleagues to lead a week-long intensive summer school in Accra, Ghana, alternating in location between the Regional Maritime University and the University of Ghana. The school receives in excess of 100 participants drawn from universities, government agencies, and the private sector organizations, mainly from Ghana and neighboring Liberia, Nigeria, Togo, and Benin, among others. The format of the school includes morning lectures, afternoon field trips, and hands-on laboratory exercises and one-on-one coaching of students. Important to the COESSING program is the satellite oceanography component which introduces participants to the extensive and often free, remotely sensed oceanographic datasets. Participants develop skills that allow them to access, process, and analyze these datasets in order to better understand regional oceanographic phenomena, such as upwelling, pollution, habitat characterization, sea level rise, and coastal erosion. Following the school, facilitators keep in touch with program participants, helping them acquire and analyze data for their studies, dissertations, and often graduate school applications, etc. In summary, schools such as COESSING are critical not only for science in the region but for the global ocean community as such training develops eager, bright minds while leading to improved regional observing and modeling strategies in severely under-sampled seas. Here, we describe a unique case in which satellite oceanography has led to such outcomes for countries bordering the Gulf of Guinea, West Africa.
      PubDate: 2022-06-01
      DOI: 10.1007/s41976-021-00051-4
       
  • Performance of the Ocean Color Algorithms: QAA, GSM, and GIOP in Inland
           and Coastal Waters

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      Abstract: Abstract The degradation of coastal waters and lakes is of a great concern due to their important roles in providing fresh water and food to the neighboring communities. Therefore, a robust monitoring plan is needed to assess the emerging water quality issues and identify the possible sources of pollution. Remote sensing could be an alternative invaluable approach for water quality monitoring compared to the traditional monitoring methods which are limited in terms of spatial coverage and temporal variability. The remote sensing–based water quality data can be estimated by modelling the apparent optical properties (AOPs) and/or the inherent optical properties (IOPs). Retrieving IOPs enables the estimation of aquatic biomass, primary production, and carbon pools. Therefore, several studies have invested significantly in improving the performance of the IOPs models to better estimate the water quality parameters. To assess uncertainty and improve IOP models in estimating water quality parameters, we review here studies of IOP modelling with a focus on coastal and inland waters. The review includes the most common IOP models: the GSM Semi-Analytical Bio-Optical Model, the Quasi-Analytical Algorithm (QAA), and the Generalized IOP Algorithm (GIOP). We review performance of these models and the regions in which they are applied in. Additionally, the limitations of the models are discussed and thus recommendations are proposed to overcome the uncertainties and incorporate better results from the models. This review could directly influence the science of future missions with the ability to monitor coastal and inland waters.
      PubDate: 2022-02-14
      DOI: 10.1007/s41976-022-00068-3
       
  • A WAVEWATCH III® Model Approach to Investigating Ocean Wave Source Terms
           for West Africa: Input-Dissipation Source Terms

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      Abstract: Abstract Input and dissipation source terms contribute significantly to the projection of ocean wave properties in numerical wave models. They form an integral part of the wave energy balance equation. This study investigates the appropriate input-dissipation source terms (Sin-ds) that best estimate the significant wave heights and wave directions in the entire West Africa region (latitudes 10° S–30° N; longitudes 35° W–15° E) and two sub-divisions (north-western or Canary Current sub-region: latitudes 10° N–25° N; longitudes 30° W–10° W, and south-eastern or Gulf of Guinea sub-region: latitudes 2° S–8° N; longitudes 10° W–10° E) using the WAVEWATCH III® (WW3) numerical ocean wave model version 5.16. Five Sin-ds (WAM Cycle 3, ST1; WAM Cycle 4 and variants, ST3; Tolman & Chalikov (1996), ST2; Ardhuin et al. (2010), ST4; and Zieger et al. (2015) ST6) and two additional variants (ST2STAB and ST4STAB) implemented in the WW3 model were investigated and outputs compared with field measured data from four stations in the region. For simulations of the sub-grids, ST2STAB best estimates significant wave heights for both the combined stations of the south-eastern grid and the north-western grid, whereas ST6 and ST2STAB best estimate wave directions for the respective sub-grids. For simulations of the entire West Africa grid, the Sin-ds that best estimate the significant wave heights are ST3, ST2STAB, ST2STAB and ST4/ST4STAB, while ST6, ST4/ST4STAB, ST2STAB and ST1 best estimate wave directions for the four respective stations. A combination of all the stations for the entire West Africa region revealed that ST2STAB best estimates significant wave heights indicated by lowest Hanna & Heinold (1985). American Petroleum Institute.) performance index (HH) and normalized bias index (NBI) values of 0.34 and −23.09% respectively. Wave directions on the other hand are best estimated by ST6 with the least NBI value and mean bias of −1.23% and −1.68±21.48°, respectively, for the entire region. ST2STAB and ST6 are thus identified to be suitable for wave height and wave direction modelling respectively for the entire West Africa region. A major conclusion of this study is that different Sin-ds best estimates the wave heights and directions in the West Africa region. However, ST2STAB would be the appropriate source terms to be used in projecting both wave height and direction since very little differences exist among the various source terms in projecting wave directions.
      PubDate: 2022-01-21
      DOI: 10.1007/s41976-021-00065-y
       
  • Inferring Lake Ice Status Using ICESat-2 Photon Data

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      Abstract: Abstract Lake ice phenology is a temporally integrated response to the seasonal cycles of meteorology, and its study results in obtaining the periods of ice growth and melt process. The recently launched spaceborne laser altimetry satellite called Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) hosts a single sensor titled Advanced Topographic Laser Altimeter System (ATLAS) is equipped with photon-counting technology. In this research, we have investigated the applicability of a data product, namely ATL03 from the ICESat-2 to infer the state of alpine lake ice. Three alpine lakes situated in the Himalayan region, which are above 4200 m ASL and exhibit annual freeze–thaw cycles were studied to understand the interaction of ICESat-2 photons with the lake surface during various phases of the ice growth and decay process. Elevation profiles were generated from the photon beams of ICESat-2 over these lakes during various stages of its surface cover like meltwater conditions, ice thickening stage, frozen state, and ice breakup period. These elevation profiles besides giving the lake surface height, the pattern of photons in the profile has contributed to envisage the status of the ice surface over the lake. Photons from ICESat-2 during meltwater conditions can penetrate the subsurface, and this feature helps in distinguishing meltwater from frozen ice cover over the lake surface. Further, it was observed that during the stage of ice thickening stage, a certain number of photons have penetrated up to a depth of ~ 35 m but the number of photons that penetrated is significantly less when compared with that of penetrating photons during meltwater conditions. Similarly, during the ice breakup periods, the photon data of ICESat-2 are proven to identify the exposed water columns in the ice sheet. The results obtained from this study prove that the photon data from ICESat-2 over alpine lakes can result in advance the understanding of the lake ice phenology.
      PubDate: 2022-01-12
      DOI: 10.1007/s41976-022-00067-4
       
  • A Complete Study on the Costliest Super Cyclone Amphan (May 2020) with Its
           Devastating Impact on West Bengal, India

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      Abstract: Abstract Amphan (16–21 May 2020) is the most intense tropical storm in the history of West Bengal during recent decades (2011–2020). After the 1999 Odisha Super Cyclone (OSuC), it is also the most intense super cyclonic storm (SuCS) that has originated over the Bay of Bengal (BoB) and caused irreparable damages during the storm event. The intensification time of Amphan was slow initially, but it grew from category 1 to 5 in record time, just within 18 h. This paper highlights mainly the pre-disaster state-level preparedness and also the severity of cyclonic storm Amphan on West Bengal. To map the flooded areas of southern West Bengal, the Sentinel-1 SAR dataset have been analyzed in the Google Earth Engine (GEE) environment. The results derived from the pixel-based analysis show that nearly 1075 km2 of land has been inundated due to intense rainfall and storm surges during Amphan. The powerful high storm surge is also responsible for the embankment breaching, which is 160 km long along the coastal belt of West Bengal.
      PubDate: 2022-01-12
      DOI: 10.1007/s41976-022-00066-5
       
  • A Global-Scale Assessment of Water Resources and Vegetation Cover Dynamics
           in Relation with the Earth Climate Gradient

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      Abstract: Abstract Changes in the terrestrial climate and the rapid growth of the world population cause pressures on water resources and natural vegetation covers. Given the importance of these resources for the survival of both human communities and the terrestrial ecosystems, it is critical to envision research-based strategies for their preservation. However, studies that assessed changes in vegetation and freshwater resources have preferentially focused on the marginal role of human, precipitation, and temperature, while neglecting the connection with global climate gradient. Yet, a full understanding of the ongoing changes in the terrestrial vegetation and water resources is needed to develop effective strategies for preserving these resources. In an effort of contributing to the understanding of these changes, this study investigates the actual patterns in the terrestrial land water masses and vegetation covers in relation with the earth climate gradient. Especially, climate aridity indices are estimated and used to highlight climate classes. Trends analyses of monthly leaf area index and land water storage anomalies show different signals depending on the earth latitude bands. Results show 36.5% of the continental lands have experienced a decrease of water resources, but these areas do not necessarily encompass regions with decreasing trends of vegetation cover. Chi-square statistics indicated significant connections between climate classes and vegetation cover trends as well as climate classes and land water storage trends. This study concludes the global climate gradient marginally regulates the dynamics of water resources and vegetation covers. Yet, examples show human-induced changes can supersede this overall connection in certain regions of the globe.
      PubDate: 2022-01-07
      DOI: 10.1007/s41976-021-00063-0
       
  • Crop Health Assessment Using Sentinel-1 SAR Time Series Data in a Part of
           Central India

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      Abstract: Abstract In India, agriculture monitoring is severely hampered by frequent cloud cover. Crop health assessment from the early growing stage onwards is vital for accurate and timely yield prediction. In this study, time series of Sentinel-1A SAR images over central India have been processed to quantify kharif crops’ growth rate. Sentinel-1 and Sentinel-2 images acquired on the same day have been used to compare the radar backscattered energy at the VH channel with normalized difference vegetation index (NDVI). A good coefficient of determination (R2 = 0.824) was found between backscatter and NDVI. It attests that the NDVI can be used in combination with SAR backscatter during the kharif season. In addition, k-means clustering classification of Sentinel-1 images indicated that the total area covered by paddy, soybean, and other crops were 103,506.86 ha, 85,390.93 ha, and 71,667.02 ha, respectively. The classification result has been validated with ground information, which has indicated an overall accuracy of 83.47%. The work indicated that radar signals’ temporal behavior is sensitive to the health status of the crops from sowing to harvesting stages. Sentinel-1 SAR images can be used to analyze kharif crops during the whole phenological cycle. The approach may serve as a solution for assessing both the health and spatial distribution of kharif crops.
      PubDate: 2022-01-03
      DOI: 10.1007/s41976-021-00064-z
       
  • Seasonal Variability of Sea Surface Salinity in the NW Gulf of Guinea from
           SMAP Satellite

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      Abstract: Abstract The advent of satellite-derived sea surface salinity (SSS) measurements has boosted scientific study in less-sampled ocean regions such as the northwestern Gulf of Guinea (NWGoG). In this study, we examine the seasonal variability of SSS in the NWGoG from the Soil Moisture Active Passive (SMAP) satellite and show that it is well-suited for such regional studies as it is able to reproduce the observed SSS features in the study region. SMAP SSS bias, relative to in-situ data comparisons, reflects the differences between skin layer measurements and bulk surface measurements that have been reported by previous studies. The study results reveal three broad anomalous SSS features: a basin-wide salinification during boreal summer, a basin-wide freshening during winter, and a meridionally oriented frontal system during other seasons. A salt budget estimation suggests that the seasonal SSS variability is dominated by changes in freshwater flux, zonal circulation, and upwelling. Freshwater flux, primarily driven by the seasonally varying Intertropical Convergence Zone, is a dominant contributor to salt budget in all seasons except during fall. Regionally, SSS is most variable off southwestern Nigeria and controlled primarily by westward extensions of the Niger River. Anomalous salty SSS off the coasts of Cote d’Ivoire and Ghana especially during summer are driven mainly by coastal upwelling and horizontal advection.
      PubDate: 2021-11-10
      DOI: 10.1007/s41976-021-00061-2
       
  • Extreme Rainfall Events over Accra, Ghana, in Recent Years

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      Abstract: Abstract This study examines the recent changes in extreme rainfall events over Accra, Ghana. For this study, an extreme rainfall event is defined as a day with rainfall equal to or exceeding the 1980–2019 95th percentile. Knowing extreme rainfall events help to identify the years with the likelihood of rainfall-related disasters in Accra. In addition, it helps to identify the years with the likelihood of drought or severe dryness which are critical for the livelihoods and economic activities of the people. The study used rainfall data from rain gauge for Accra and satellite-derived winds at the 850 hPa level over southern Ghana from 1980 to 2019. It compares these climatic parameters for both pre-2000 and post-2000 to find out the changes that have occurred throughout the study period. Results show that the frequency and magnitude of extreme rainfall have generally increased during the post-2000 period than during the pre-2000 period, causing increases in mortalities and damages to properties. Seasonally, extreme rainfall events were most intense in July during the pre-2000 period but have changed to June during the post-2000 period. Notably, more intense rainfall events have also occurred during post-2000 winter than pre-2000 winter, consistent with increased warming in the study area. Monthly mean meridional winds at the 850 hPa level were stronger (weaker) in the northerly (southerly) direction during the pre-2000 period but have changed to be stronger (weaker) in the southerly (northerly) direction during the post-2000 period.
      PubDate: 2021-11-09
      DOI: 10.1007/s41976-021-00062-1
       
  • Earth Observation Services in Support of West Africa’s Blue Economy:
           Coastal Resilience and Climate Impacts

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      Abstract: Abstract The marine and coastal resources of the West Africa region contribute immensely to the global economy as well as to the economies of countries in the region. The region boasts of two vibrant Large Marine Ecosystems (LMEs), namely the Canary Current Large Marine Ecosystem (CCLME) and the Guinea Current Large Marine Ecosystem (GCLME), which provide vast fisheries and other marine resources. The region and its marine and coastal resources are however faced with diverse threats such as climate change, destruction of mangroves, overfishing, habitat destruction and coastal erosion, among many others. Several initiatives have been developed to address these challenges. This paper reviews some of the past as well as ongoing initiatives that address the challenges in the marine and coastal environment of West Africa. Among these initiatives is the Global Monitoring for Environment and Security (GMES) and Africa programme which uses Earth Observation (EO) data and derived information to manage marine and coastal resources, with focus on the “Marine and Coastal Areas Management in western Africa” theme implemented by the University of Ghana (UG). The Regional Marine Centre (RMC) at the University of Ghana, which serves as the Regional Implementation Centre for the GMES and Africa marine programme for West Africa, uses sentinel-1 satellite data to monitor shoreline change. This information is combined with other datasets to generate coastal vulnerability indices (CVI) map for erosion hotspots in the region, which directly feeds into policy initiatives towards addressing the problem of coastal erosion in the region. This, as a result, contributes to building coastal resilience and alleviating the severe impacts of climate change on the West Africa coast, and contributing to the Blue Economy agenda.
      PubDate: 2021-10-19
      DOI: 10.1007/s41976-021-00058-x
       
  • Capacity Strengthening Towards Application of Earth Observation Tools and
           Services to Enhancing Marine and Coastal Areas Management in West Africa

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      Abstract: Abstract Capacity development forms the core and contributes to the success and sustainability of every emerging field and technology. The use of Earth Observation (EO) space technology, though not new, requires a substantial amount of capacity for its sustainable application, especially in the West Africa sub-region, where knowledge of such technology and its applications is minimal. As part of the drive to adopt EO applications and space technology in Africa, the European Commission (EC) and the African Union Commission (AUC) instituted several EO initiatives to encourage African countries to utilise EO tools and services to aid their decision-making processes. Among these initiatives is the Monitoring for Environment and Security in Africa (MESA) project (2013 to 2017), which was succeeded by the ongoing Global Monitoring for Environment and Security and Africa (GMES & Africa) programme. The implementation of the MESA and GMES & Africa programmes has, at their core, capacity development strategies to help use EO data and services for mitigating the numerous challenges facing the marine and coastal areas of the sub-region. The University of Ghana, being the lead implementing institution of these initiatives for the marine domain, for the West Africa region, embarked on several capacity strengthening activities to support the use of EO tools and services in addressing the challenges of marine and coastal areas. These activities span from regional online and onsite meetings, national face-to-face training, internships, fellowships, innovation challenges and formation of open-source clubs. More than 1,150 participants, from 14 beneficiary coastal countries and more than 130 academic and research institutions in marine and coastal areas, national institutions, start-ups companies, private sector and NGOs, were trained within the West Africa region. At the regional level, 36.72% constituted female trainees. The training covered areas such as EO data access, monitoring biological indicators for fisheries management, monitoring ocean conditions for ensuring safety at sea, and monitoring and mapping coastal habitats and land use.
      PubDate: 2021-10-02
      DOI: 10.1007/s41976-021-00057-y
       
  • Evaluation of ECMWF and NCEP Reanalysis Wind Fields for Long-Term
           Historical Analysis and Ocean Wave Modelling in West Africa

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      Abstract: Abstract Ocean wind fields form a significant input to ocean wave models. This study evaluates the accuracy of two major reanalysis wind fields: NCEP-NCAR reanalysis-II (NNR-II) and ECMWF ERA5 wind datasets in the marine domain of West Africa. The objective is to identify the reanalysis data that best represents the wind regimes of the sub-region for use in climate studies and ocean wave modelling. The reanalysis datasets were validated against in situ measurements from PIRATA meteorological buoys in the region. Both reanalysis datasets indicate good agreement with in situ measurements and capture the variability in the wind fields. However, ERA5 wind fields outperform the NNR-II wind fields and better represents the variability in wind fields in the region. They display higher correlation coefficients and R-squared values as well as lower bias and RMSE values for all wind components at all PIRATA buoy locations. Correlation coefficients of 0.92, 0.87, 0.94, and 0.98; R-squared values of 0.83, 0.76, 0.89, and 0.96; mean bias of −0.34±0.75 ms−1, 0.25±33.75°, 0.07   ±   0.86 ms−1, and −0.21±0.96 ms−1; and RMSE values of 0.82 ms−1, 33.75°, 0.87 ms−1, and 0.98 ms−1 were observed for ERA5 resolved wind speeds, wind directions, and zonal and meridional winds respectively. NNR-II also recorded correlation coefficients of 0.64, 0.7, 0.73, and 0.9; R-squared values of 0.19, 0.39, 0.32, and 0.79; mean bias of 0.12±1.77 ms−1, 8.91±53.43°, 0.55±2.09 ms−1, and −0.31±2.15 ms−1; and RMSE values of 1.77 ms−1, 54.17°, 2.16 ms−1, and 2.17 ms−1 for resolved wind speeds, wind directions, and zonal and meridional winds, respectively. NNR-II winds tend to highly overestimate zonal wind speeds and underestimate meridional wind speeds. Meridional winds are better predicted compared to zonal winds for both NNR-II and ERA5 winds. There was a general overestimation of lower wind speeds and underestimation of higher wind speeds on the part of both reanalysis datasets although this assertion varied with geographical location. To enhance the accuracy of resolved wind velocities and directions in the region, there is the need to improve the estimation of zonal winds in general by both NNR-II and ERA5 winds but with much efforts needed for NNR-II. In effect, ERA5 reanalysis winds better describe the wind regime of West Africa for climate studies and ocean wave modelling.
      PubDate: 2021-08-18
      DOI: 10.1007/s41976-021-00052-3
       
 
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