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Comparing the performance of turbulent kinetic energy and K-profile parameterization vertical parameterization schemes over the tropical indian ocean / Lokesh Kumar Pandey in Marine geodesy, vol 44 n° 1 (January 2021)  

Public [article]  inMarine geodesy > vol 44 n° 1 (January 2021) . - pp 42 - 69 Titre : Comparing the performance of turbulent kinetic energy and K-profile parameterization vertical parameterization schemes over the tropical indian ocean Type de document : Article/Communication Auteurs : Lokesh Kumar Pandey, Auteur ; Suneet Dwivedi, Auteur Année de publication : 2021 Article en page(s) : pp 42 - 69 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Océanographie [Termes descripteurs IGN] Bengale, golfe du [Termes descripteurs IGN] énergie cinétique [Termes descripteurs IGN] Indien (océan) [Termes descripteurs IGN] modélisation spatiale [Termes descripteurs IGN] mousson [Termes descripteurs IGN] salinité [Termes descripteurs IGN] température de surface de la mer Résumé : (Auteur) The performance of vertical parameterization schemes, namely, turbulent kinetic energy (TKE) and K-profile parameterization (KPP), is evaluated over the domain [30E-120E; 20S-30N] in the Indian Ocean using the Nucleus for European Modeling of the Ocean (NEMO) regional model. The surface and sub-surface hydrography and mixed layer depth (MLD) of the simulations using TKE and KPP schemes have been compared. The KPP scheme produces higher bias (∼0.5 °C) of sea surface temperature (SST) in monsoon and post-monsoon seasons, which reduces on using the TKE scheme. The maximum surface salinity difference (0.45 psu) between TKE and KPP simulations is obtained over the head Bay of Bengal (BoB) in the post-monsoon months. The KPP scheme also overestimates MLD of the region. Barring highly convective regions as well as regions marked with very low and rapidly changing salinity, the TKE scheme performs better than KPP scheme in simulating the hydrography and MLD of the region. The differences between TKE and KPP simulations in the vertical stability and mixing are studied using buoyancy frequency, vertical shear of horizontal currents and energy required for mixing as quantifiers. The mixed layer heat budget analysis explains seasonal variability of SST and differences in vertical mixing parameterizations. Numéro de notice : A2021-059 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01490419.2020.1835758 date de publication en ligne : 29/10/2020 En ligne : https://doi.org/10.1080/01490419.2020.1835758 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96849 [article]

Characterizing the spatial and temporal variation of the land surface temperature hotspots in Wuhan from a local scale / Chen Yang in Geo-spatial Information Science, vol 23 n° 4 (December 2020)  

Public [article]  inGeo-spatial Information Science > vol 23 n° 4 (December 2020) . - pp 327 - 340 Titre : Characterizing the spatial and temporal variation of the land surface temperature hotspots in Wuhan from a local scale Type de document : Article/Communication Auteurs : Chen Yang, Auteur ; Qingming Zhan, Auteur ; Sihang Gao, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 327 - 340 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] climat urbain [Termes descripteurs IGN] géomorphologie locale [Termes descripteurs IGN] ilot thermique urbain [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] image Terra-MODIS [Termes descripteurs IGN] image thermique [Termes descripteurs IGN] morphologie urbaine [Termes descripteurs IGN] processus gaussien [Termes descripteurs IGN] regroupement de données [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] température au sol [Termes descripteurs IGN] Wuhan (Chine) [Termes descripteurs IGN] zonage (urbanisme) Résumé : (auteur) Land Surface Temperature (LST) derived from space-borne Thermal-infrared (TIR) sensors is a key parameter of urban climate studies. Current studies are inefficient to capture the spatial and temporal variations of LST for only one snapshot adopted at one time. Focusing on the characterization of the spatial and temporal of LST variations at local scales, the latent patterns, and morphological characteristics are extracted in this study. Technically, sixteen MODerate-resolution Imaging Spectroradiometer (MODIS) eight-day synthesized LST products (MYD11A2) in 2002, 2007, 2012, and 2017 are employed. First, the non-parametric Multi-Task Gaussian Process Model (MTGP) is used to extract the smooth and continuous Latent LST (LLST) patterns using one LST subset and its temporally adjacent images. Second, the Multi-Scale Shape Index (MSSI) is then applied to quantify the morphological characteristics at the optimal scale. Then, the LLST patterns and MSSI maps are clustered into multiple spatial categories. The specific clusters with the highest LLST and MSSI values are considered as local LLST hotspots. The Hotspots Weighted Mean Center (HSWMC) and standard deviation ellipse are adopted to further investigate the spatiotemporal change of hotspots orientation, direction, and trajectories. Results revealed that Impervious Surfaces (IS) composition is the most significant external forcing of local LST anomalies. The configuration factors (e.g., shape index, aggregation index) also have a noticeable local warming effect. This study represents a latent pattern and morphology-based framework for LST hotspots spatial and temporal variations characterization, catering to the zoning and grading strategies in urban planning. Numéro de notice : A2020-788 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2020.1834882 date de publication en ligne : 06/11/2020 En ligne : https://doi.org/10.1080/10095020.2020.1834882 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96550 [article]

A deep learning approach to improve the retrieval of temperature and humidity profiles from a ground-based microwave radiometer / Xing Yan in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 58 n° 12 (December 2020) . - pp 8427 - 8437 Titre : A deep learning approach to improve the retrieval of temperature and humidity profiles from a ground-based microwave radiometer Type de document : Article/Communication Auteurs : Xing Yan, Auteur ; Chen Liang, Auteur ; Yize Jiang, Auteur Année de publication : 2020 Article en page(s) : pp 8427 - 8437 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] humidité du sol [Termes descripteurs IGN] modèle atmosphérique [Termes descripteurs IGN] radiomètre à hyperfréquence [Termes descripteurs IGN] température au sol Résumé : (auteur) The ground-based microwave radiometer (MWR) retrieves atmospheric profiles with a high temporal resolution for temperature and humidity up to a height of 10 km. Such profiles are critical for understanding the evolution of climate systems. To improve the accuracy of profile retrieval in MWR, we developed a deep learning approach called batch normalization and robust neural network (BRNN). In contrast to the traditional backpropagation neural network (BPNN), which has previously been applied for MWR profile retrieval, BRNN reduces overfitting and has a greater capacity to describe nonlinear relationships between MWR measurements and atmospheric structure information. Validation of BRNN with the radiosonde demonstrates a good retrieval capability, showing a root-mean-square error of 1.70 K for temperature, 11.72% for relative humidity (RH), and 0.256 g/m 3 for water vapor density. A detailed comparison with various inversion methods (BPNN, extreme gradient boosting, support vector machine, ridge regression, and random forest) has also been conducted in this research, using the same training and test data sets. From the comparison, we demonstrated that BRNN significantly improves retrieval accuracy, particularly for the retrieval of temperature and RH near the surface. Numéro de notice : A2020-741 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2987896 date de publication en ligne : 29/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2987896 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96371 [article]

Analysis of the effect of climate warming on paludification processes: Will soil conditions limit the adaptation of Northern boreal forests to climate change? A synthesis / Ahmed Laamrani in Forests, vol 11 n°11 (November 2020)  

Public [article]  inForests > vol 11 n°11 (November 2020) . - n° 1176 Titre : Analysis of the effect of climate warming on paludification processes: Will soil conditions limit the adaptation of Northern boreal forests to climate change? A synthesis Type de document : Article/Communication Auteurs : Ahmed Laamrani, Auteur ; Osvaldo Valeria, Auteur ; Abdelghani Chehbouni, Auteur ; Yves Bergeron, Auteur Année de publication : 2020 Article en page(s) : n° 1176 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] Canada [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] écosystème forestier [Termes descripteurs IGN] forêt boréale [Termes descripteurs IGN] humidité du sol [Termes descripteurs IGN] paludification [Termes descripteurs IGN] précipitation [Termes descripteurs IGN] température au sol [Termes descripteurs IGN] tourbe [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Northern boreal forests are characterized by accumulation of accumulation of peat (e.g., known as paludification). The functioning of northern boreal forest species and their capacity to adapt to environmental changes appear to depend on soil conditions. Climate warming is expected to have particularly pronounced effects on paludified boreal ecosystems and can alter current forest species composition and adaptation by changing soil conditions such as moisture, temperature regimes, and soil respiration. In this paper, we review and synthesize results from various reported studies (i.e., 88 research articles cited hereafter) to assess the effects of climatic warming on soil conditions of paludified forests in North America. Predictions that global warming may increase the decomposition rate must be considered in combination with its impact on soil moisture, which appears to be a limiting factor. Local adaptation or acclimation to current climatic conditions is occurring in boreal forests, which is likely to be important for continued ecosystem stability in the context of climate change. The most commonly cited response of boreal forest species to global warming is a northward migration that tracks the climate and soil conditions (e.g., temperature and moisture) to which they are adapted. Yet, some constraints may influence this kind of adaptation, such as water availability, changes in fire regimes, decomposer adaptations, and the dynamic of peat accumulation. In this paper, as a study case, we examined an example of potential effects of climatic warming on future paludification changes in the eastern lowland region of Canada through three different combined hypothetical scenarios based on temperature and precipitation (e.g., unchanged, increase, or decrease). An increase scenario in precipitation will likely favor peat accumulation in boreal forest stands prone to paludification and facilitate forested peatland expansion into upland forest, while decreased or unchanged precipitation combined with an increase in temperature will probably favor succession of forested peatlands to upland boreal forests. Each of the three scenarios were discussed in this study, and consequent silvicultural treatment options were suggested for each scenario to cope with anticipated soil and species changes in the boreal forests. We concluded that, despite the fact boreal soils will not constrain adaptation of boreal forests, some consequences of climatic warming may reduce the ability of certain species to respond to natural disturbances such as pest and disease outbreaks, and extreme weather events. Numéro de notice : A2020-759 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f11111176 date de publication en ligne : 07/11/2020 En ligne : https://doi.org/10.3390/f11111176 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96472 [article]

Sea surface temperature and high water temperature occurrence prediction using a long short-term memory model / Minkyu Kim in Remote sensing, vol 12 n° 21 (November 2020)  

Public [article]  inRemote sensing > vol 12 n° 21 (November 2020) . - n° 3654 Titre : Sea surface temperature and high water temperature occurrence prediction using a long short-term memory model Type de document : Article/Communication Auteurs : Minkyu Kim, Auteur ; Hung Yang, Auteur ; Jonghwa Kim, Auteur Année de publication : 2020 Article en page(s) : n° 3654 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] aquaculture [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] Corée du sud [Termes descripteurs IGN] données météorologiques [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] pêche [Termes descripteurs IGN] réseau neuronal récurrent [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] température de surface de la mer Résumé : (auteur) Recent global warming has been accompanied by high water temperatures (HWTs) in coastal areas of Korea, resulting in huge economic losses in the marine fishery industry due to disease outbreaks in aquaculture. To mitigate these losses, it is necessary to predict such outbreaks to prevent or respond to them as early as possible. In the present study, we propose an HWT prediction method that applies sea surface temperatures (SSTs) and deep-learning technology in a long short-term memory (LSTM) model based on a recurrent neural network (RNN). The LSTM model is used to predict time series data for the target areas, including the coastal area from Goheung to Yeosu, Jeollanam-do, Korea, which has experienced frequent HWT occurrences in recent years. To evaluate the performance of the SST prediction model, we compared and analyzed the results of an existing SST prediction model for the SST data, and additional external meteorological data. The proposed model outperformed the existing model in predicting SSTs and HWTs. Although the performance of the proposed model decreased as the prediction interval increased, it consistently showed better performance than the European Center for Medium-Range Weather Forecast (ECMWF) prediction model. Therefore, the method proposed in this study may be applied to prevent future damage to the aquaculture industry. Numéro de notice : A2020-721 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs12213654 date de publication en ligne : 07/11/2020 En ligne : https://doi.org/10.3390/rs12213654 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96311 [article]

A preliminary exploration of the cooling effect of tree shade in urban landscapes / Qiuyan Yu in International journal of applied Earth observation and geoinformation, vol 92 (October 2020)  

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Spatio-temporal relationship between land cover and land surface temperature in urban areas: A case study in Geneva and Paris / Xu Ge in ISPRS International journal of geo-information, vol 9 n° 10 (October 2020)  

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Deriving a frozen area fraction from Metop ASCAT backscatter based on Sentinel-1 / Helena Bergstedt in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)  

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Photoperiod and temperature as dominant environmental drivers triggering secondary growth resumption in Northern Hemisphere conifers / Jian-Guo Huang in Proceedings of the National Academy of Sciences of the United States of America PNAS, vol 117 n° 34 (August 2020)  

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Extraction of urban built-up areas from nighttime lights using artificial neural network / Tingting Xu in Geocarto international, vol 35 n° 10 ([01/08/2020])  

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Roles of horizontal and vertical tree canopy structure in mitigating daytime and nighttime urban heat island effects / Jike Chen in International journal of applied Earth observation and geoinformation, vol 89 (July 2020)  

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Developing shopping and dining walking indices using POIs and remote sensing data / Yingbin Deng in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)  

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Estimating spatio-temporal air temperature in London (UK) using machine learning and earth observation satellite data / Rochelle Schneider dos Santos in International journal of applied Earth observation and geoinformation, vol 88 (June 2020)  

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Assessment of winter season land surface temperature in the Himalayan regions around the Kullu area in India using Landsat-8 data / Divyesh Varade in Geocarto international, vol 35 n° 6 ([01/05/2020])  

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Temporal Validation of Four LAI Products over Grasslands in the Northeastern Tibetan Plateau / Gaofei Yin in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 4 (April 2020)  

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