Evaluation of Sentinel-1 &amp; 2 time series for predicting wheat and rapeseed phenological stages

Mercier, Audrey; Betbeder, Julie; Baudry, Jacques; Roux, Vincent Le; Spicher, Fabien; Lacoux, Jérôme; Roger, David; Hubert‐Moy, Laurence

doi:10.1016/j.isprsjprs.2020.03.009

Cited by 114 publications

(90 citation statements)

References 57 publications

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“…Despite this noise, a distinct seasonality is observed for CR. This finding is in agreement with the observed crop temporal profiles of Mercier et al (2020) (wheat and rapeseed), Nasrallah et al (2019) (wheat) and with Frison et al (2018) and Proisy et al (2000) who studied the CR temporal behaviour over mixed temperate forests. In addition, CR temporal evolution coincides with that of NDVI derived from S2, depicting a VI increase in the early and late spring for winter and summer crops, and a sharp decrease in summer.…”

Section: Discussionsupporting

confidence: 92%

“…Our results show potential for the use of all-weather S1 observations to retrieve LSP, as illustrated by the correlation between CR S1- and NDVI S2-derived LSP ( Table 3 ). This observation agrees with Mercier et al (2020) who found that CR and NDVI, among various features extracted from S1 and S2 data, importantly contributed to obtaining accurate classifications of phenological stages of selected fields of wheat and rapeseed.…”

Section: Discussionsupporting

confidence: 91%

“…All these studies recognised that more research is needed to clarify the usefulness of CR because changes in soil roughness and vegetation structure also affect its temporal behaviour to some extent. Mercier et al (2020) evaluated the potential of Sentinel-1 and -2 data to retrieve wheat and rapeseed phenological stages with a classification approach over a limited set of fields in northern France. The study showed that the combined use of input features from both sensors improved the accuracy in the classification as compared to the use of each sensor alone.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparing land surface phenology of major European crops as derived from SAR and multispectral data of Sentinel-1 and -2

Meroni

d’Andrimont

Vrieling

et al. 2021

Remote Sensing of Environment

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Section: Discussionsupporting

confidence: 92%

Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparing land surface phenology of major European crops as derived from SAR and multispectral data of Sentinel-1 and -2

Meroni

d’Andrimont

Vrieling

et al. 2021

Remote Sensing of Environment

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“…Satellite constellations allowing a temporal revisit of the order of a few days, coupled with a free-access policy have emerged in the recent years. Possibilities of Earth Observation applications in monitoring vegetation status have steadily increased (Frampton et al, 2013, Mercier et al, 2020. The Sentinel satellites of the European Copernicus program allow the joint exploitation of optical and SAR data with similar high spatial * Corresponding author resolution, with global coverage, and with a temporal revisit of 5/6 days at mid-latitudes.…”

Section: Contextmentioning

confidence: 99%

On the Joint Exploitation of Optical and Sar Satellite Imagery for Grassland Monitoring

Garioud

Valero

Giordano

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Time series of optical and Synthetic Aperture RADAR (SAR) images provide complementary knowledge about the cover and use of the Earth surface since they exhibit information of distinct physical nature. They have proved to be particularly relevant for monitoring large areas with high temporal dynamics and related to significant ecosystem services. Grasslands are such crucial surfaces, both in terms of economic and environmental issues and the automatic and frequent monitoring of their agricultural practices is required for many purposes. To address this problem, the deep-based SenDVI framework is presented. SenDVI proposes an object-based methodology to estimate NDVI values from Sentinel-1 SAR observations and contextual knowledge (weather, terrain). Values are regressed every 6 days for compliance with monitoring purposes. Very satisfactory results are obtained with this low-level multimodal fusion strategy (R2 = 0.84 on a Sentinel-2 tile). Finer analysis is however required to fully assess the relevance of each modality (Sentinel-1, Sentinel-2, weather, terrain) and feature sets and to propose the simplest conceivable framework. Results show that not all features are necessary and can be discarded while others have a mandatory contribution to the regression task. Moreover, experiments prove that accuracy can be improved by not saturating the network with non-essential information (among contextual knowledge in particular). This allows to move towards more operational solution.

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“…Some phenological stages are more sensitive to biotic or abiotic impacts. The Sentinel-2 time series offers a good opportunity to analyze phonological stages and transition dates [ 89 , 91 , 92 ]. The high variability of the vegetation cover cycle is analyzed in this study to detect change.…”

Section: Discussionmentioning

confidence: 99%

Unsupervised Monitoring Vegetation after the Closure of an Ore Processing Site with Multi-Temporal Optical Remote Sensing

Fabre

Gimenez

Elger

et al. 2020

Sensors

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Ore processing is a source of soil heavy metal pollution. Vegetation traits (structural characteristics such as spatial cover and repartition; biochemical parameters—pigment and water contents, growth rate, phenological cycle…) and plant species identity are indirect and powerful indicators of residual contamination detection in soil. Multi-temporal multispectral satellite imagery, such as the Sentinel-2 time series, is an operational environment monitoring system widely used to access vegetation traits and ensure vegetation surveillance across large areas. For this purpose, methodology based on a multi-temporal fusion method at the feature level is applied to vegetation monitoring for several years from the closure and revegetation of an ore processing site. Features are defined by 26 spectral indices from the literature and seasonal and annual change detection maps are inferred. Three indices—CIred-edge (CIREDEDGE), IRECI (Inverted Red-Edge Chlorophyll Index) and PSRI (Plant Senescence Reflectance Index)—are particularly suitable for detecting changes spatially and temporally across the study area. The analysis is conducted separately for phyto-stabilized vegetation zones and natural vegetation zones. Global and specific changes are emphasized and explained by information provided by the site operator or meteorological conditions.

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Evaluation of Sentinel-1 & 2 time series for predicting wheat and rapeseed phenological stages

Cited by 114 publications

References 57 publications

Comparing land surface phenology of major European crops as derived from SAR and multispectral data of Sentinel-1 and -2

Comparing land surface phenology of major European crops as derived from SAR and multispectral data of Sentinel-1 and -2

On the Joint Exploitation of Optical and Sar Satellite Imagery for Grassland Monitoring

Unsupervised Monitoring Vegetation after the Closure of an Ore Processing Site with Multi-Temporal Optical Remote Sensing

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