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Combining machine learning and compact polarimetry for estimating soil moisture from C-Band SAR data / Emanuele Santi in Remote sensing, Vol 11 n° 20 (October-2 2019)
[article]
Titre : Combining machine learning and compact polarimetry for estimating soil moisture from C-Band SAR data Type de document : Article/Communication Auteurs : Emanuele Santi, Auteur ; Mohammed Dabboor, Auteur ; Simone Pettinato, Auteur ; Simonetta Paloscia, Auteur Année de publication : 2019 Article en page(s) : 18 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage automatique
[Termes IGN] bande C
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] humidité du sol
[Termes IGN] image radar moirée
[Termes IGN] image Radarsat
[Termes IGN] Manitoba (Canada)
[Termes IGN] polarimétrie
[Termes IGN] polarisation
[Termes IGN] réseau neuronal artificiel
[Termes IGN] série temporelle
[Termes IGN] surface cultivéeRésumé : (auteur) This research aimed at exploiting the joint use of machine learning and polarimetry for improving the retrieval of surface soil moisture content (SMC) from synthetic aperture radar (SAR) acquisitions at C-band. The study was conducted on two agricultural areas in Canada, for which a series of RADARSAT-2 (RS2) images were available along with direct measurements of SMC from in situ stations. The analysis confirmed the sensitivity of RS2 backscattering (O°) to SMC. The comparison of SMC with the compact polarimetry (CP) parameters, computed from the RS2 acquisitions by the CP data simulator, pointed out that some CP parameters had a sensitivity to SMC equal or better than O°, with correlation coe?cients up to R ' 0.4. Based on these results, the potential of machine learning (ML) for SMC retrieval was exploited by implementing and testing on the available data an artificial neural network (ANN) algorithm. The algorithm was implemented using several combinations of O° and CP parameters. Validation results of the algorithm with in situ observations confirmed the promising capabilities of the ML techniques for SMC monitoring. Furthermore, results pointed out the potential of CP in improving the SMC retrieval accuracy, especially when used in combination with linearly polarized O°. Depending on the considered input combination, the ANN algorithm was able to estimate SMC with Root Mean Square Error (RMSE) between 3% and 7% of SMC and R between 0.7 and 0.9. Numéro de notice : A2019-555 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs11202451 Date de publication en ligne : 22/10/2019 En ligne : https://doi.org/10.3390/rs11202451 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94210
in Remote sensing > Vol 11 n° 20 (October-2 2019) . - 18 p.[article]Comparative analysis of the accuracy of surface soil moisture estimation from the C- and L-bands / Mohammad El Hajj in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)
[article]
Titre : Comparative analysis of the accuracy of surface soil moisture estimation from the C- and L-bands Type de document : Article/Communication Auteurs : Mohammad El Hajj, Auteur ; Nicolas Baghdadi, Auteur ; Mehrez Zribi, Auteur Année de publication : 2019 Article en page(s) : 13 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] analyse comparative
[Termes IGN] bande C
[Termes IGN] bande L
[Termes IGN] humidité du sol
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Normalized Difference Water Index
[Termes IGN] réseau neuronal artificiel
[Termes IGN] surface cultivéeRésumé : (auteur) Surface soil moisture (SSM) estimation is of great importance in several areas, such as hydrology, agriculture and risk assessment. C-band SAR (synthetic aperture radar) data have been widely used to estimate SSM, whereas few studies have been performed using L-band SAR due to the low availability of L-band SAR data. In this context, the objective of the present paper is to compare the SSM estimation potentials of the C- (Sentinel-1) and L-bands (PALSAR) for wheat and grassland plots. The inversion approach developed in this study uses neural networks to invert the SAR signal and estimate the SSM. For each radar frequency, the developed neural networks were trained using the following as an input vector: SAR incidence angle, SAR polarization (VV for the C-band and HH for the L-band), and NDVI from optical images. Artificial Neural networks (ANNs) were developed and validated using synthetic and real databases. The results showed that the L-band provided slightly less accurate SSM estimates than the C-band. Moreover, the results showed that the accuracies of the SSM estimates for both frequencies strongly depended on the soil roughness (Hrms) and SSM values. From the synthetic database at SSM values less than 25 vol.%, the ANNs underestimated the SSM for Hrms values less than 1.5 cm and overestimated the SSM for Hrms values greater than 1.5 cm. In addition, the ANNs underestimated the SSM value regardless of the Hrms value when the SSM value was greater than 25 vol.%. An RMSE analysis of the SSM estimates showed that the highest RMSE values were observed for the L-band regardless of the SSM value, and high RMSE values were observed for the C-band only in very wet soil conditions (SSM>25 vol.%). From the real database at NDVI values less than 0.7, the RMSE (root mean square error) of the SSM estimates was 4.6 vol.% for the C-band and 5.3 vol.% for the L-band. Most importantly, the L-band enabled the estimation of the SSM under a well-developed vegetation cover (NDVI > 0.7) with an RMSE of 6.7 vol.%, whereas the C-band SAR signal became completely attenuated for some crops when the NDVI value was greater than 0.7, and thus the estimation of SSM was impossible using the C-band. Numéro de notice : A2019-473 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.05.021 Date de publication en ligne : 29/06/2019 En ligne : https://doi.org/10.1016/j.jag.2019.05.021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93634
in International journal of applied Earth observation and geoinformation > vol 82 (October 2019) . - 13 p.[article]The relationship between climate and the intra-annual oxygen isotope patterns from pine trees: a case study along an elevation gradient on Corsica, France / Sonja Szymczak in Annals of Forest Science, Vol 76 n° 3 (September 2019)
[article]
Titre : The relationship between climate and the intra-annual oxygen isotope patterns from pine trees: a case study along an elevation gradient on Corsica, France Type de document : Article/Communication Auteurs : Sonja Szymczak, Auteur ; Achim Bräuning, Auteur ; Martin Häusser, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : 14 p Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] changement climatique
[Termes IGN] Corse
[Termes IGN] croissance des arbres
[Termes IGN] dendrologie
[Termes IGN] gradient d'altitude
[Termes IGN] humidité du sol
[Termes IGN] isotope
[Termes IGN] oxygène (O²)
[Termes IGN] Pinus nigra corsicana
[Termes IGN] Pinus pinaster
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Key message: Intra-annual oxygen isotope patterns of tree-ring cellulose from Pinus nigra subsp. laricio and Pinus pinaster along an elevation gradient can be divided into four distinct patterns ((1) high values in the beginning of the tree ring, declining trend afterwards; (2) low values in the beginning, increasing trend afterwards; (3) high values in the middle of the tree ring; (4) declining trend over the whole tree ring). These patterns reflect the dependency on local site conditions and different water sources.
Context: Intra-annual oxygen isotope chronologies from tree-ring cellulose can serve as a proxy for past climate conditions at inter- or even intra-seasonal resolution.
Aims: We explore the potential of these chronologies from pine trees (Pinus nigra J.F. Arn subsp. laricio (Poiret) Maire var Corsicana Hyl. and Pinus pinaster Aiton) growing on the Mediterranean island of Corsica, France, along an elevation gradient ranging from sea level to 1600 m asl.
Methods: We developed high-resolution intra-annual oxygen isotope chronologies from tree-ring cellulose of eight trees from five sites along the elevation gradient. The analysis period covers the decade 2007–2016 for seven trees and the period 1987–2016 for one tree, respectively.
Results: The chronologies show a high variability between trees, sites, and years. We identified four different intra-annual oxygen isotope patterns which reflect the dependency of tree growth on different water sources (precipitation during the growing season vs. deeper soil water reservoirs) and climate conditions during the growth season. Trees at coastal sites have access to groundwater, while trees growing in high elevations mostly depend on precipitation during the growth season as water source.
Conclusion: The different patterns can be attributed to the use of different water sources. The intra-annual chronology covering 30 years indicates an increasing frequency of dry-warm summers after 2007.Numéro de notice : A2019-304 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-019-0860-9 Date de publication en ligne : 29/07/2019 En ligne : https://doi.org/10.1007/s13595-019-0860-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93237
in Annals of Forest Science > Vol 76 n° 3 (September 2019) . - 14 p[article]Using LiDAR-modified topographic wetness index, terrain attributes with leaf area index to improve a single-tree growth model in south-eastern Finland / Cheikh Mohamedou in Forestry, an international journal of forest research, vol 92 n° 3 (July 2019)
[article]
Titre : Using LiDAR-modified topographic wetness index, terrain attributes with leaf area index to improve a single-tree growth model in south-eastern Finland Type de document : Article/Communication Auteurs : Cheikh Mohamedou, Auteur ; Lauri Korhonen, Auteur ; Kalle Eerikäinen, Auteur ; Timo Tokola, Auteur Année de publication : 2019 Article en page(s) : pp 253 - 263 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] croissance des arbres
[Termes IGN] diamètre des arbres
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] erreur systématique
[Termes IGN] Finlande
[Termes IGN] humidité du sol
[Termes IGN] indice d'humidité
[Termes IGN] Leaf Area Index
[Termes IGN] modèle de croissance végétale
[Termes IGN] Perceptron multicoucheRésumé : (Auteur) Tree growth information is crucial in forest management and planning. Terrain-derived attributes such as the topographic wetness index (TWI), in addition to leaf area index (LAI) are closely related to tree growth, but are not commonly used in empirical growth models. In this study, we examined if modified TWI and LAI estimated from airborne light detection and ranging (LiDAR) data could be used to improve the predictions of a national single-tree diameter growth model. Altogether 1118 sample trees were selected within 197 subjectively placed plots in randomly selected forest stands in south-eastern Finland. Linear mixed effect (LME) and multilayer perceptron models were used to model the bias of 5-year growth predictions of the model and thus ultimately improve its predictions. The root mean square error (RMSE) of the national model was 0.604 cm. LME modelling reduced this value to 0.404 cm and MLP to 0.568 cm. The predictors included in the best-performing LME model were modified TWI, LAI estimated from LiDAR intensities, and elevation. Without an LAI estimate, the best RMSE was 0.436 cm. When applied as such, original and modified TWIs produced similar accuracy. We conclude that both TWI and LAI obtained from LiDAR data improve the diameter growth predictions of the national model. Numéro de notice : A2019-293 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1093/forestry/cpz010 Date de publication en ligne : 28/02/2019 En ligne : https://doi.org/10.1093/forestry/cpz010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93184
in Forestry, an international journal of forest research > vol 92 n° 3 (July 2019) . - pp 253 - 263[article]Comprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data / P. Kumar in Geocarto international, vol 34 n° 9 ([15/06/2019])
[article]
Titre : Comprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data Type de document : Article/Communication Auteurs : P. Kumar, Auteur ; A. Choudhary, Auteur ; D. K. Gupta, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 1022-1041 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande C
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] couvert végétal
[Termes IGN] échantillonnage d'image
[Termes IGN] humidité du sol
[Termes IGN] image radar moirée
[Termes IGN] image Radarsat
[Termes IGN] image Sentinel-SAR
[Termes IGN] modèle de régression
[Termes IGN] polarisation
[Termes IGN] réseau neuronal artificiel
[Termes IGN] Uttar Pradesh (Inde ; état)Résumé : (auteur) In the present study, random forest regression (RFR), support vector regression (SVR) and artificial neural network regression (ANNR) models were evaluated for the retrieval of soil moisture covered by winter wheat, barley and corn crops. SVR with radial basis function kernel was provided the highest adj. R2 (0.95) value for soil moisture retrieval covered by the wheat crop at VV polarization. However, RFR provided the adj. R2 (0.94) value for soil moisture retrieval covered by barley crop at VV polarization using Sentinel-1A satellite data. The adj. R2 (0.94) values were found for the soil moisture covered by corn crop at VV polarization using RFR, SVR linear and radial basis function kernels. The least performance was reported using ANNR model for almost all the crops under investigation. The soil moisture retrieval outcomes were found better at VV polarization in comparison to VH polarization using three different models. Numéro de notice : A2019-517 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1464601 Date de publication en ligne : 03/05/2018 En ligne : https://doi.org/10.1080/10106049.2018.1464601 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93876
in Geocarto international > vol 34 n° 9 [15/06/2019] . - pp 1022-1041[article]Réservation
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