Vegetation Cartography from Sentinel‐1 Radar Images

Frison, Patrice; Lardeux, Cédric

doi:10.1002/9781119457107.ch6

Cited by 7 publications

(6 citation statements)

References 7 publications

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“…Both the radar backscattering coefficient σ 0 and the modulus of the interferometric coherence |ρ| were analyzed. After orthorectifying the passes [16], σ 0 was extracted from the GRDH products using python scripts [17] implemented in QGIS software and based on Orfeo Toolbox Software [18]. These open-source tools, designed to process multiple Sentinel images automatically, can be used on a common PC (4 GB RAM) (A program to install the customized QGIS software in the Windows or Linux environments is available from http://remotesensing4all.net/index.php/en/2018/09/20/qgisremote-sensing-kit-2/).…”

Section: Sentinel-1 Datamentioning

confidence: 99%

Potential of Sentinel-1 Data for Monitoring Temperate Mixed Forest Phenology

et al. 2018

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In this study, the potential of Sentinel-1 data to seasonally monitor temperate forests was investigated by analyzing radar signatures observed from plots in the Fontainebleau Forest of the Ile de France region, France, for the period extending from March 2015 to January 2016. Radar backscattering coefficients, σ0 and the amplitude of temporal interferometric coherence profiles in relation to environmental variables are shown, such as in situ precipitation and air temperature. The high temporal frequency of Sentinel-1 acquisitions (i.e., twelve days, or six, if both Sentinel-1A and B are combined over Europe) and the dual polarization configuration (VV and VH over most land surfaces) made a significant contribution. In particular, the radar backscattering coefficient ratio of VV to VH polarization, σVV0/σVH0, showed a well-pronounced seasonality that was correlated with vegetation phenology, as confirmed in comparison to NDVI profiles derived from Landsat-8 (r=0.77) over stands of deciduous trees. These results illustrate the high potential of Sentinel-1 data for monitoring vegetation, and as these data are not sensitive to the atmosphere, the phenology could be estimated with more accuracy than optical data. These observations will be quantitatively analyzed with the use of electromagnetic models in the near future.

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Section: Sentinel-1 Datamentioning

confidence: 99%

Potential of Sentinel-1 Data for Monitoring Temperate Mixed Forest Phenology

et al. 2018

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“…Indeed, it has been shown that after sowing, roughness is affected by very limited temporal variations (Bousbih et al, 2017) as no soil works occur after sowing. It is usually kept constant during the crop season (El Hajj et al, 2016;Gherboudj et al, 2011;Gorrab et al, 2015;Ouaadi et al, 2020b).…”

Section: Surface Roughnessmentioning

confidence: 99%

“…The samples are weighed first in the field to obtain fresh aboveground biomass (FAGB). The corresponding aboveground biomass (AGB) expressed in kilograms of dry matter per square meter is determined at the laboratory by drying the samples in an electric oven at 105 • C for 48 h. The vegetation water content (VWC) is thus computed as the difference between FAGB and AGB (Gherboudj et al, 2011).…”

Section: Biomass and Water Contentmentioning

confidence: 99%

“…The sensitivity of the backscattering coefficient to the surface soil moisture (SSM) is widely documented in the literature for bare or covered soils Ouaadi et al, 2020c, b;Ulaby and Dobson, 1986;Zribi et al, 2014). Several retrieval approaches based on the inversion of a radiative transfer model (Bai et al, 2017;Gherboudj et al, 2011;El Hajj et al, 2016;Li and Wang, 2018;Ouaadi et al, 2020b) or based on linear or non-linear empirical regression (Gorrab et al, 2015;Ouaadi et al, 2020b) have been developed. The SSM derived from radar observations is also used to estimate RZSM (root zone soil moisture), a key variable in agronomy, through the combination with a land surface model (Cho et al, 2015;Das et al, 2008;Dumedah et al, 2015;Ford et al, 2014;Rodell et al, 2004;Sabater et al, 2006;Sure and Dikshit, 2019).…”

Section: Introductionmentioning

confidence: 99%

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C-band radar data and in situ measurements for the monitoring of wheat crops in a semi-arid area (center of Morocco)

Ouaadi

Ezzahar

Khabba

et al. 2021

Earth Syst. Sci. Data

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Abstract. A better understanding of the hydrological functioning of irrigated crops using remote sensing observations is of prime importance in semi-arid areas where water resources are limited. Radar observations, available at high resolution and with a high revisit time since the launch of Sentinel-1 in 2014, have shown great potential for the monitoring of the water content of the upper soil and of the canopy. In this paper, a complete set of data for radar signal analysis is shared with the scientific community for the first time to our knowledge. The data set is composed of Sentinel-1 products and in situ measurements of soil and vegetation variables collected during three agricultural seasons over drip-irrigated winter wheat in the Haouz plain in Morocco. The in situ data gather soil measurements (time series of half-hourly surface soil moisture, surface roughness and agricultural practices) and vegetation measurements collected every week/2 weeks including aboveground fresh and dry biomasses, vegetation water content based on destructive measurements, the cover fraction, the leaf area index, and plant height. Radar data are the backscattering coefficient and the interferometric coherence derived from Sentinel-1 GRDH (Ground Range Detected High Resolution) and SLC (Single Look Complex) products, respectively. The normalized difference vegetation index derived from Sentinel-2 data based on Level-2A (surface reflectance and cloud mask) atmospheric-effects-corrected products is also provided. This database, which is the first of its kind made available open access, is described here comprehensively in order to help the scientific community to evaluate and to develop new or existing remote sensing algorithms for monitoring wheat canopy under semi-arid conditions. The data set is particularly relevant for the development of radar applications including surface soil moisture and vegetation variable retrieval using either physically based or empirical approaches such as machine and deep learning algorithms. The database is archived in the DataSuds repository and is freely accessible via the following DOI: https://doi.org/10.23708/8D6WQC (Ouaadi et al., 2020a).

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“…Les images Sentinel-1 en bande C (λ 5,6 cm) comprennent des données GRD de niveau 1 en mode interférométrique large (IW). La calibration radiométrique est l'une des premières étapes importantes pour le prétraitement des images radar (Frison et Lardeux, 2018 ;Lopez et al, 2020), qui convertit les valeurs numériques en rétrodiffusion (c'est-à-dire les signaux radar renvoyés) en sigma zéro ( ) en décibels (dB). Le calcul du coefficient de rétrodiffusion peut être effectué selon l'équation (Laur et al, 2004 ;Denize et al, 2019) [Eq.…”

Section: Acquisition Et Traitement Des Donnéesunclassified

Complémentarité des images optiques SENTINEL-2 avec les images radar SENTINEL-1 et ALOS-PALSAR-2 pour la cartographie de la couverture végétale : application à une aire protégée et ses environs au Nord-Ouest du Maroc via trois algorithmes d’apprentissage automatique.

Acharki

Frison²,

Amharref³

et al. 2021

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Dans cet article, nous évaluons les performances de classification de trois algorithmes non paramétriques (kNN, RF et SVM) en utilisant les données multi-temporelles de trois satellites (Sentinel-1, Alos-Palsar-2 et Sentinel-2) et de leurs combinaisons. La zone d'étude choisie se caractérise par un climat méditerranéen subhumide et une topographie très accidentée qui rend la classification d’occupation du sol particulièrement difficile. En outre, elle contient une aire protégée nommée Jbel Moussa et présente une diversité biologique exceptionnelle. Afin de suivre le couvert végétal de cette dernière, nous avons acquis et prétraités les images satellitaires optiques et radar pour la période du 1er janvier au 31 décembre 2017. Ensuite, nous avons combiné les trois satellites, soit douze scénarios produits. Des cartes de classifications illustrent notre approche. Un total de trente-six classifications a été obtenu, en se basant sur sept classes : eau, bâtiment et infrastructures, sol nu, végétation peu dense, prairies, forêt peu dense et forêt dense. Les résultats ont montré que pour tous les scénarios, la précision globale la plus élevée a été produite par RF (53,03%-93,06%), suivie de kNN (49,16%-89,63%), tandis que SVM (47,86%-86,08%) a produit la précision de classification la plus faible. L'étude a également montré une similitude entre les performances de la combinaison des trois satellites et celles de Sentinel-2 seul. Les estimations de la superficie pour les différentes classes vont de 0,85 km2 (0,11% de la zone d'étude) à 326,84 km2 (41,31% de la zone d'étude)

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Vegetation Cartography from Sentinel‐1 Radar Images

Cited by 7 publications

References 7 publications

Potential of Sentinel-1 Data for Monitoring Temperate Mixed Forest Phenology

Potential of Sentinel-1 Data for Monitoring Temperate Mixed Forest Phenology

C-band radar data and in situ measurements for the monitoring of wheat crops in a semi-arid area (center of Morocco)

Complémentarité des images optiques SENTINEL-2 avec les images radar SENTINEL-1 et ALOS-PALSAR-2 pour la cartographie de la couverture végétale : application à une aire protégée et ses environs au Nord-Ouest du Maroc via trois algorithmes d’apprentissage automatique.

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