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Parcel-based summer maize mapping and phenology estimation combined using Sentinel-2 and time series Sentinel-1 data / Yanyan Wang in International journal of applied Earth observation and geoinformation, vol 108 (April 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 108 (April 2022) . - n° 102720 Titre : Parcel-based summer maize mapping and phenology estimation combined using Sentinel-2 and time series Sentinel-1 data Type de document : Article/Communication Auteurs : Yanyan Wang, Auteur ; Shenghui Fang, Auteur ; Lingli Zhao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102720 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] carte de la végétation [Termes IGN] Chine [Termes IGN] croissance végétale [Termes IGN] données spatiotemporelles [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] maïs (céréale) [Termes IGN] mesure de similitude [Termes IGN] phénologie [Termes IGN] saison [Termes IGN] segmentation d'image [Termes IGN] série temporelle [Termes IGN] surface cultivée Résumé : (auteur) This study aims to map the planting area of summer maize and estimate the spatiotemporal phenology information with parcel-based classification method through integration of Sentinel-1/2 data in Jiaozuo located in North China Plain. For the maize mapping, the combination of Sentinel-1/2 data with the parcel-based method has the highest classification accuracy, suggesting that the integration of Sentinel-1/2 data with parcel-based method has great potential for regional maize mapping. For the estimation of maize phenology, the dynamic threshold method is used to extract the tasseling and milk ripening date through the time series σ0VH. In order to reduce the influence of precipitation or irrigation on SAR data, a Local Minimum Value Composite (LMVC) method is proposed to filter the original time series SAR data. The systematic phenology estimation method mainly includes LMVC, S-G filtering, Fourier curve fitting and dynamic threshold points extracting. Compared with the actual phenology date by field investigation, the errors of estimated tasseling and milk ripening date are 4.3 days and 5.5 days respectively, indicating that the time series σ0VH derived from the SAR data has great potential in spatiotemporal phenology estimation of field maize. Finally, the scattering mechanism of the maize field to C-band microwave in different growth periods was analyzed. It was also found that the phenology of maize was delayed in the coal mining subsidence areas and the areas with insufficient field management. Numéro de notice : A2022-232 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102720 Date de publication en ligne : 24/02/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102720 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100121 [article]

Projections of climate change impacts on flowering-veraison water deficits for Riesling and Müller-Thurgau in Germany / Chenyao Yang in Remote sensing, vol 14 n° 6 (March-2 2022)  

Public [article]  inRemote sensing > vol 14 n° 6 (March-2 2022) . - n° 1519 Titre : Projections of climate change impacts on flowering-veraison water deficits for Riesling and Müller-Thurgau in Germany Type de document : Article/Communication Auteurs : Chenyao Yang, Auteur ; Christoph Menz, Auteur ; Maxim Simões De Abreu Jaffe, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1519 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Allemagne [Termes IGN] changement climatique [Termes IGN] données météorologiques [Termes IGN] phénologie [Termes IGN] sécheresse [Termes IGN] stress hydrique [Termes IGN] viticulture [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) With global warming, grapevine is expected to be increasingly exposed to water deficits occurring at various development stages. In this study, we aimed to investigate the potential impacts of projected climate change on water deficits from the flowering to veraison period for two main white wine cultivars (Riesling and Müller-Thurgau) in Germany. A process-based soil-crop model adapted for grapevine was utilized to simulate the flowering-veraison crop water stress indicator (CWSI) of these two varieties between 1976–2005 (baseline) and 2041–2070 (future period) based on a suite of bias-adjusted regional climate model (RCM) simulations under RCP4.5 and RCP8.5. Our evaluation indicates that the model can capture the early-ripening (Müller-Thurgau) and late-ripening (Riesling) traits, with a mean bias of prediction of ≤2 days and a well-reproduced inter-annual variability for more than 60 years. Under climate projections, the flowering stage is advanced by 10–20 days (higher in RCP8.5) between the two varieties, whereas a slightly stronger advancement is found for Müller-Thurgau than for Riesling for the veraison stage. As a result, the flowering-veraison phenophase is mostly shortened for Müller-Thurgau, whereas it is extended by up to two weeks for Riesling in cool and high-elevation areas. The length of phenophase plays an important role in projected changes of flowering-veraison mean temperature and precipitation. The late-ripening trait of Riesling makes it more exposed to increased summer temperature (mainly in August), resulting in a higher mean temperature increase for Riesling (1.5–2.5 °C) than for Müller-Thurgau (1–2 °C). As a result, an overall increased CWSI by up to 15% (ensemble median) is obtained for both varieties, whereas the upper (95th) percentile of simulations shows a strong signal of increased water deficit by up to 30%, mostly in the current winegrowing regions. Intensified water deficit stress can represent a major threat for high-quality white wine production, as only mild water deficits are acceptable. Nevertheless, considerable variabilities of CWSI were discovered among RCMs, highlighting the importance of efforts towards reducing uncertainties in climate change impact assessment. Numéro de notice : A2022-252 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/rs14061519 Date de publication en ligne : 21/03/2022 En ligne : https://doi.org/10.3390/rs14061519 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100208 [article]

Deep-learning-based multispectral image reconstruction from single natural color RGB image - Enhancing UAV-based phenotyping / Jiangsan Zhao in Remote sensing, vol 14 n° 5 (March-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 5 (March-1 2022) . - n° 1272; Titre : Deep-learning-based multispectral image reconstruction from single natural color RGB image - Enhancing UAV-based phenotyping Type de document : Article/Communication Auteurs : Jiangsan Zhao, Auteur ; Ajay Kumar, Auteur ; Balaji Naik Banoth, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1272; Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] agriculture de précision [Termes IGN] apprentissage profond [Termes IGN] erreur absolue [Termes IGN] image multibande [Termes IGN] image RVB [Termes IGN] Inde [Termes IGN] phénologie [Termes IGN] reconstruction d'image Résumé : (auteur) Multispectral images (MSIs) are valuable for precision agriculture due to the extra spectral information acquired compared to natural color RGB (ncRGB) images. In this paper, we thus aim to generate high spatial MSIs through a robust, deep-learning-based reconstruction method using ncRGB images. Using the data from the agronomic research trial for maize and breeding research trial for rice, we first reproduced ncRGB images from MSIs through a rendering model, Model-True to natural color image (Model-TN), which was built using a benchmark hyperspectral image dataset. Subsequently, an MSI reconstruction model, Model-Natural color to Multispectral image (Model-NM), was trained based on prepared ncRGB (ncRGB-Con) images and MSI pairs, ensuring the model can use widely available ncRGB images as input. The integrated loss function of mean relative absolute error (MRAEloss) and spectral information divergence (SIDloss) were most effective during the building of both models, while models using the MRAEloss function were more robust towards variability between growing seasons and species. The reliability of the reconstructed MSIs was demonstrated by high coefficients of determination compared to ground truth values, using the Normalized Difference Vegetation Index (NDVI) as an example. The advantages of using “reconstructed” NDVI over Triangular Greenness Index (TGI), as calculated directly from RGB images, were illustrated by their higher capabilities in differentiating three levels of irrigation treatments on maize plants. This study emphasizes that the performance of MSI reconstruction models could benefit from an optimized loss function and the intermediate step of ncRGB image preparation. The ability of the developed models to reconstruct high-quality MSIs from low-cost ncRGB images will, in particular, promote the application for plant phenotyping in precision agriculture. Numéro de notice : A2022-210 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14051272 Date de publication en ligne : 05/03/2022 En ligne : https://doi.org/10.3390/rs14051272 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100033 [article]

Comparing methods to extract crop height and estimate crop coefficient from UAV imagery using structure from motion / Nitzan Malachy in Remote sensing, vol 14 n° 4 (February-2 2022)  

Public [article]  inRemote sensing > vol 14 n° 4 (February-2 2022) . - n° 810 Titre : Comparing methods to extract crop height and estimate crop coefficient from UAV imagery using structure from motion Type de document : Article/Communication Auteurs : Nitzan Malachy, Auteur ; Imri Zadak, Auteur ; Offer Rozenstein, Auteur Année de publication : 2022 Article en page(s) : n° 810 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] analyse spectrale [Termes IGN] covariance [Termes IGN] cultures [Termes IGN] données lidar [Termes IGN] hauteur de la végétation [Termes IGN] hétérogénéité spatiale [Termes IGN] image captée par drone [Termes IGN] modèle de croissance végétale [Termes IGN] régression linéaire [Termes IGN] série temporelle [Termes IGN] structure-from-motion [Termes IGN] zone d'intérêt Résumé : (auteur) Although it is common to consider crop height in agricultural management, variation in plant height within the field is seldom addressed because it is challenging to assess from discrete field measurements. However, creating spatial crop height models (CHMs) using structure from motion (SfM) applied to unmanned aerial vehicle (UAV) imagery can easily be done. Therefore, looking into intra- and inter-season height variability has the potential to provide regular information for precision management. This study aimed to test different approaches to deriving crop height from CHM and subsequently estimate the crop coefficient (Kc). CHMs were created for three crops (tomato, potato, and cotton) during five growing seasons, in addition to manual height measurements. The Kc time-series were derived from eddy-covariance measurements in commercial fields and estimated from multispectral UAV imagery in small plots, based on known relationships between Kc and spectral vegetation indices. A comparison of four methods (Mean, Sample, Median, and Peak) was performed to derive single height values from CHMs. Linear regression was performed between crop height estimations from CHMs against manual height measurements and Kc. Height was best predicted using the Mean and the Sample methods for all three crops (R2 = 0.94, 0.84, 0.74 and RMSE = 0.056, 0.071, 0.051 for cotton, potato, and tomato, respectively), as was the prediction of Kc (R2 = 0.98, 0.84, 0.8 and RMSE = 0.026, 0.049, 0.023 for cotton, potato, and tomato, respectively). The Median and Peak methods had far less success in predicting both, and the Peak method was shown to be sensitive to the size of the area analyzed. This study shows that CHMs can help growers identify spatial heterogeneity in crop height and estimate the crop coefficient for precision irrigation applications. Numéro de notice : A2022-139 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14040810 Date de publication en ligne : 09/02/2022 En ligne : https://doi.org/10.3390/rs14040810 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99774 [article]

Conservation zones increase habitat heterogeneity of certified Mediterranean oak woodlands / Teresa Mexia in Forest ecology and management, vol 504 (15 January 2022)  

Public [article]  inForest ecology and management > vol 504 (15 January 2022) . - n° 119811 Titre : Conservation zones increase habitat heterogeneity of certified Mediterranean oak woodlands Type de document : Article/Communication Auteurs : Teresa Mexia, Auteur ; Xavier Lecomte, Auteur ; Maria Conceição Caldeira, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 119811 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biométrie [Termes IGN] certification forestière [Termes IGN] chênaie [Termes IGN] conservation des ressources forestières [Termes IGN] écosystème forestier [Termes IGN] forêt méditerranéenne [Termes IGN] gestion forestière durable [Termes IGN] Portugal [Termes IGN] Quercus suber [Termes IGN] régénération (sylviculture) [Termes IGN] sous-étage [Termes IGN] sylvopastoralisme [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) Forest certification is a conservation tool, which aims to promote the sustainable management and conservation of forest ecosystems. Establishing set-aside or lower intervention conservation zones to promote biodiversity conservation is a requisite of forest certification. We tested the effects of conservation zones on the tree biometrics and regeneration, as well as on the taxonomic, functional, and structural diversity of the shrub and herb understory, in Mediterranean oak woodlands. We also assessed how oak biometrics, regeneration and understory diversity varied among conservation zones established 10, 14, and 20 years before our sampling dates. Oak regeneration tended to be higher in conservation zones than in controls, but results varied with the age of conservation zones. For example, the abundance of oak seedlings and saplings was higher in 10-year-old conservation zones than in those established 20 years ago. Abundance of young oak trees was higher in 14-year-old conservation zones than in 10- and 20-year-old conservation zones. The understory vertical diversity was significantly higher in 14- and 20-year-old conservation zones than in controls. Functional diversity differed significantly between conservation zones and controls, with a higher abundance of late-successional shrubs, namely fleshy-fruited species in 20-year-old conservation zones. The plant species composition of the shrub and the herb understory was most dissimilar between older conservation zones and controls. Additionally, the cover and diversity of the understory herb species decreased with the age of conservation zones. Conservation zones implemented under forest certification increase habitat structural complexity of oak woodlands, which may benefit wildlife species, but there will be trade-offs with the cover and diversity of the herb understory. Forest managers must evaluate such trade-offs when establishing conservation zones in cork oak woodlands under forest certification schemes. Numéro de notice : A2022-019 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article DOI : 10.1016/j.foreco.2021.119811 Date de publication en ligne : 02/11/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119811 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99213 [article]

Latent heat flux variability and response to drought stress of black poplar: A multi-platform multi-sensor remote and proximal sensing approach to relieve the data scarcity bottleneck / Flavia Tauro in Remote sensing of environment, vol 268 (January 2022)  

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Monitoring and analysis of crop irrigation dynamics in Central Italy through the use of MODIS NDVI data / Marta Chiesi in European journal of remote sensing, vol 55 n° 1 (January 2022)  

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Particle swarm optimization based water index (PSOWI) for mapping the water extents from satellite images / Mohammad Hossein Gamshadzaei in Geocarto international, vol 36 n° 20 ([01/12/2021])  

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Crop rotation modeling for deep learning-based parcel classification from satellite time series / Félix Quinton in Remote sensing, vol 13 n° 22 (November-2 2021)  

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Potential flood hazard zone mapping based on geomorphologic considerations and fuzzy analytical hierarchy model in a data scarce West African basin / Olabanji Aladejana in Geocarto international, vol 36 n° 19 ([01/11/2021])  

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Field scale wheat LAI retrieval from multispectral Sentinel 2A-MSI and LandSat 8-OLI imagery: effect of atmospheric correction, image resolutions and inversion techniques / Rajkumar Dhakar in Geocarto international, vol 36 n° 18 ([01/10/2021])  

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Estimating regional soil moisture with synergistic use of AMSR2 and MODIS images / Majid Rahimzadegan in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 9 (September 2021)  

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Geoglam, l'agriculture par satellite / Laurent Polidori in Géomètre, n° 2194 (septembre 2021)

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Sentinel-1 sensitivity to soil moisture at high incidence angle and the impact on retrieval over seasonal crops / Davide Palmisano in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)  

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An integrated methodology for surface soil moisture estimating using remote sensing data approach / Rida Khellouk in Geocarto international, vol 36 n° 13 ([15/07/2021])  

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