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Estimating crop type and yield of small holder fields in Burkina Faso using multi-day Sentinel-2 / Akiko Elders in Remote Sensing Applications: Society and Environment, RSASE, Vol 27 (August 2022)  

Public [article]  inRemote Sensing Applications: Society and Environment, RSASE > Vol 27 (August 2022) . - n° 100820 Titre : Estimating crop type and yield of small holder fields in Burkina Faso using multi-day Sentinel-2 Type de document : Article/Communication Auteurs : Akiko Elders, Auteur ; Mark Carroll, Auteur ; Christopher S.R. Neigh, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 100820 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Burkina Faso [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] image Sentinel-MSI [Termes IGN] parcelle agricole [Termes IGN] régression harmonique [Termes IGN] rendement agricole [Termes IGN] variation saisonnière Résumé : (auteur) Remote Sensing affords the opportunity to monitor and evaluate data scarce regions where field collection efforts are costly. A particular challenge is monitoring and evaluation in regions with smallholder agricultural systems (∼1 ha) that are often subsistence focused, vulnerable to food insecurity and data scarce. Using multi-day moderate resolution Sentinel-2 and Random Forest models, this study shows that crop type and rice yields in Burkina Faso can be predicted with greater than ∼80% accuracy in the rainy season. Model optimization using varying spectral and vegetation index inputs can increase crop type and yield prediction accuracy in the dry season where denser cultivation is a challenge for the 10–20 m resolution of Sentinel-2. However, there is a trade-off between opting for very high-resolution imagery ( Numéro de notice : A2022-624 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rsase.2022.100820 Date de publication en ligne : 02/08/2022 En ligne : https://doi.org/10.1016/j.rsase.2022.100820 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101391 [article]

Monitoring of phenological stage and yield estimation of sunflower plant using Sentinel-2 satellite images / Omer Gokberk Narin in Geocarto international, vol 37 n° 5 ([01/03/2022])  

Public [article]  inGeocarto international > vol 37 n° 5 [01/03/2022] . - pp 1378 - 1392 Titre : Monitoring of phenological stage and yield estimation of sunflower plant using Sentinel-2 satellite images Type de document : Article/Communication Auteurs : Omer Gokberk Narin, Auteur ; Saygin Abdikan, Auteur Année de publication : 2022 Article en page(s) : pp 1378 - 1392 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] image multitemporelle [Termes IGN] image Sentinel-MSI [Termes IGN] indice de végétation [Termes IGN] phénologie [Termes IGN] rendement agricole [Termes IGN] tournesol [Termes IGN] Turquie Résumé : (Auteur) With the increase of the world’s population, while urbanization is increasing, agricultural lands are decreasing. Therefore, monitoring of up-to-date agricultural lands is important for agricultural product estimation. The study investigates suitability of Sentinel-2 data for the phenological stage analysis and yield estimation of sunflower plant. To this aim, fieldworks was conducted and sunflower parcels were identified in Zile district of Tokat province, Turkey which has dense sunflower production. In this study, ten Vegetation Indices (VIs) were performed by using multi-temporal Sentinel-2 data obtained during the growth stages of sunflower plant and yield estimation was obtained. As a result, the indices obtained on 30 June, at the stage of inflorescence emergence, provided coefficient of determination (R2) higher than 0.67 and The Root Mean Square Error (RMSE) lower than 13 kg/da. Among the VIs, the best forecast obtained by NDVI (R2 = 0.74 and RMSE = 10.80 kg/da) approximately three months before the harvest of sunflower. Numéro de notice : A2022-276 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1765886 Date de publication en ligne : 25/05/2020 En ligne : https://doi.org/10.1080/10106049.2020.1765886 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100784 [article]

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Evaluation of sum-NDVI values to estimate wheat grain yields using multi-temporal Landsat OLI data / Asadollah Mirasi in Geocarto international, vol 36 n° 12 ([01/07/2021])  

Public [article]  inGeocarto international > vol 36 n° 12 [01/07/2021] . - pp 1309-1304 Titre : Evaluation of sum-NDVI values to estimate wheat grain yields using multi-temporal Landsat OLI data Type de document : Article/Communication Auteurs : Asadollah Mirasi, Auteur ; Asghar Mahmoudi, Auteur ; Hossein Navid, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1309-1304 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse comparative [Termes IGN] blé (céréale) [Termes IGN] données de terrain [Termes IGN] image Landsat-OLI [Termes IGN] image Terra-MODIS [Termes IGN] Leaf Area Index [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] rendement agricole [Termes IGN] Soil Adjusted Vegetation Index Résumé : (Auteur) Normalized difference vegetation index (NDVI)-based models have been developed to derive wheat grain yields with multispectral images. In this regard, field measurements and Landsat 8 Operational Land Imager (OLI) data were used for two growing seasons to determine the relationships between NDVI and yields. The number of six statistic parameters were calculated from NDVI values to find the best agreement with actual yield data. A comparison of the results showed that sum-NDVI better matched field measurements. To compare the results of NDVI with other vegetation indices, we applied four other vegetation indices. Results indicated that estimation of wheat yields using sum-NDVI values was more accurate than estimation by sum of the four applied vegetation indices values. Also, the investigation of multi-temporal images showed that the critical time to estimate wheat yield using sum-NDVI values was the time that wheat grains were in the milky and maturity stages. Numéro de notice : A2021-377 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1641561 Date de publication en ligne : 16/07/2019 En ligne : https://doi.org/10.1080/10106049.2019.1641561 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97872 [article]

Evaluating the performance of hyperspectral leaf reflectance to detect water stress and estimation of photosynthetic capacities / Jingjing Zhou in Remote sensing, vol 13 n° 11 (June-1 2021)  

Public [article]  inRemote sensing > vol 13 n° 11 (June-1 2021) . - n° 2160 Titre : Evaluating the performance of hyperspectral leaf reflectance to detect water stress and estimation of photosynthetic capacities Type de document : Article/Communication Auteurs : Jingjing Zhou, Auteur ; Ya-Hao Zhang, Auteur ; Ze-Min Han, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 2160 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] Chine [Termes IGN] Citrus (genre) [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] feuille (végétation) [Termes IGN] image hyperspectrale [Termes IGN] photosynthèse [Termes IGN] réflectance végétale [Termes IGN] rendement agricole [Termes IGN] stress hydrique [Termes IGN] surveillance de la végétation Résumé : (auteur) Advanced techniques capable of early, rapid, and nondestructive detection of the impacts of drought on fruit tree and the measurement of the underlying photosynthetic traits on a large scale are necessary to meet the challenges of precision farming and full prediction of yield increases. We tested the application of hyperspectral reflectance as a high-throughput phenotyping approach for early identification of water stress and rapid assessment of leaf photosynthetic traits in citrus trees by conducting a greenhouse experiment. To this end, photosynthetic CO2 assimilation rate (Pn), stomatal conductance (Cond) and transpiration rate (Trmmol) were measured with gas-exchange approaches alongside measurements of leaf hyperspectral reflectance from citrus grown across a gradient of soil drought levels six times, during 20 days of stress induction and 13 days of rewatering. Water stress caused Pn, Cond, and Trmmol rapid and continuous decline throughout the entire drought period. The upper layer was more sensitive to drought than middle and lower layers. Water stress could also bring continuous and dynamic changes of the mean spectral reflectance and absorptance over time. After trees were rewatered, these differences were not obvious. The original reflectance spectra of the four water stresses were surprisingly of low diversity and could not track drought responses, whereas specific hyperspectral spectral vegetation indices (SVIs) and absorption features or wavelength position variables presented great potential. The following machine-learning algorithms: random forest (RF), support vector machine (SVM), gradient boost (GDboost), and adaptive boosting (Adaboost) were used to develop a measure of photosynthesis from leaf reflectance spectra. The performance of four machine-learning algorithms were assessed, and RF algorithm yielded the highest predictive power for predicting photosynthetic parameters (R2 was 0.92, 0.89, and 0.88 for Pn, Cond, and Trmmol, respectively). Our results indicated that leaf hyperspectral reflectance is a reliable and stable method for monitoring water stress and yield increase, with great potential to be applied in large-scale orchards. Numéro de notice : A2021-440 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13112160 Date de publication en ligne : 31/05/2021 En ligne : https://doi.org/10.3390/rs13112160 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97826 [article]

Leaf area index estimation of wheat crop using modified water cloud model from the time-series SAR and optical satellite data / Vijay Pratap Yadav in Geocarto international, vol 36 n° 7 ([15/04/2021])  

Public [article]  inGeocarto international > vol 36 n° 7 [15/04/2021] . - pp 791 - 802 Titre : Leaf area index estimation of wheat crop using modified water cloud model from the time-series SAR and optical satellite data Type de document : Article/Communication Auteurs : Vijay Pratap Yadav, Auteur ; Rajendra Prasad, Auteur ; Ruchi Bala, Auteur Année de publication : 2021 Article en page(s) : pp 791 - 802 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] blé (céréale) [Termes IGN] image radar moirée [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] Inde [Termes IGN] Leaf Area Index [Termes IGN] polarisation [Termes IGN] rendement agricole [Termes IGN] série temporelle Résumé : (Auteur) The time-series synthetic aperture radar (SAR) and optical satellite data were used for the leaf area index (LAI) estimation of wheat crop using modified water cloud model (MWCM) in Varanasi district, India. In this study, MWCM was developed by including scale invariant vegetation fraction (fveg) in the old WCM for the estimation of LAI. The non-linear least square optimization technique was applied to determine the optimum model parameters for the retrieval of LAI which was further validated with the observed LAI. The estimated values of LAI by MWCM at VV polarization shows good correspondence (R2 = 0.901 and RMSE = 0.456 m2/m2) with the observed LAI values than at VH polarization (R2 = 0.742 and RMSE = 0.521 m2/m2).The MWCM shows great potential for the LAI estimation of wheat crop by incorporating optical data (i.e. Sentinel-2) in terms of fveg with SAR data (i.e. Sentinel-1A). Numéro de notice : A2021-294 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1624984 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1624984 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97352 [article]

A CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery / Lucas Prado Osco in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)  

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Amélioration des systèmes de suivi des cultures à l’aide de la télédétection multi-source et des techniques d’apprentissage profond / Yawogan Gbodjo (2021)  

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Counting of grapevine berries in images via semantic segmentation using convolutional neural networks / Laura Zabawa in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)  

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Discriminant analysis for lodging severity classification in wheat using RADARSAT-2 and Sentinel-1 data / Sugandh Chauhan in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)  

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Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging / Bo Li in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)  

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Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods / Elisa Kamir in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

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Three-dimensional photogrammetric mapping of cotton bolls in situ based on point cloud segmentation and clustering / Shangpeng Sun in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

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Spatio-Temporal Prediction of the Epidemic Spread of Dangerous Pathogens Using Machine Learning Methods / Wolfgang B. Hamer in ISPRS International journal of geo-information, Vol 9 n° 1 (January 2020)  

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Automatic canola mapping using time series of Sentinel 2 images / Davoud Ashourloo in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)  

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Détection de changement par imagerie radar sur les zones naturelles et agricoles en milieu tropical / Jérôme Lebreton (2018) 

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