Satellite-based monitoring of grassland: assessment of harvest dates and frequency using SAR

Siegmund, Robert; Grant, K.; Wagner, M.; Hartmann, Stephan

doi:10.1117/12.2240947

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…As a result, these data are affected by climatic events (rain, frost, humidity), as well as by the effects of ground relief. Although monitoring of grasslands with SAR data only shows satisfactory results (Buckley, Smith, 2010, Siegmund et al, 2016, Tamm et al, 2016, Chiboub et al, 2019, most of studies are either relying on full polarimetry (giving access to the Radar Vegetation Index) or on Very High Resolution (VHR) data. Both are difficult to access and not compliant with dense monitoring requirements.…”

Section: Problem Statementmentioning

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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Section: Problem Statementmentioning

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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“…Either various management strategies, such as grazing, mowing or mixed, were classified [124][125][126], or the focus was on the intensity and the classification was applied to detect different degrees of use intensity of the grasslands [127][128][129]. In studies focusing on mown grasslands, classifications were applied to detect mowing events during the growing season [130][131][132].…”

Section: Methods Used In Remote Sensing Of Grassland Management and Umentioning

confidence: 99%

Remote Sensing of Grassland Production and Management—A Review

2020

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Grasslands cover one third of the earth’s terrestrial surface and are mainly used for livestock production. The usage type, use intensity and condition of grasslands are often unclear. Remote sensing enables the analysis of grassland production and management on large spatial scales and with high temporal resolution. Despite growing numbers of studies in the field, remote sensing applications in grassland biomes are underrepresented in literature and less streamlined compared to other vegetation types. By reviewing articles within research on satellite-based remote sensing of grassland production traits and management, we describe and evaluate methods and results and reveal spatial and temporal patterns of existing work. In addition, we highlight research gaps and suggest research opportunities. The focus is on managed grasslands and pastures and special emphasize is given to the assessment of studies on grazing intensity and mowing detection based on earth observation data. Grazing and mowing highly influence the production and ecology of grassland and are major grassland management types. In total, 253 research articles were reviewed. The majority of these studies focused on grassland production traits and only 80 articles were about grassland management and use intensity. While the remote sensing-based analysis of grassland production heavily relied on empirical relationships between ground-truth and satellite data or radiation transfer models, the used methods to detect and investigate grassland management differed. In addition, this review identified that studies on grassland production traits with satellite data often lacked including spatial management information into the analyses. Studies focusing on grassland management and use intensity mostly investigated rather small study areas with homogeneous intensity levels among the grassland parcels. Combining grassland production estimations with management information, while accounting for the variability among grasslands, is recommended to facilitate the development of large-scale continuous monitoring and remote sensing grassland products, which have been rare thus far.

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“…Radiometric and geometric discontinuities between modalities have then to be considered [2]. To address low optical temporal resolution, exponential amount of work is being done to investigate SAR time series for PG monitoring and MowEve assessment as in [9] with COSMO-SkyMed data, and in [10] for PG classification from RapidEye augmented with TerraSAR-X data. The potential of dual-pol capability of S1 to discriminate MowEve and grazing is discussed in [5].…”

Section: B Methodological Analysis: Related Workmentioning

confidence: 99%

Challenges in Grassland Mowing Event Detection with Multimodal Sentinel Images

Garioud¹,

Giordano²,

Valero

et al. 2019

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)

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Permanent Grasslands (PG) are heterogeneous environments with high spatial and temporal dynamics, subject to increasing environmental challenges. This study aims to identify requirements, key constraining factors and solutions for robust and complete detection of Mowing Events. Remote sensing is a powerful tool to monitor and investigate Near-Real-Time and seasonally PG cover. Here, pros and cons of Sentinel-2 (S2) and Sentinel-1 (S1) time series exploitation for Mowing Events (MowEve) detection are analysed. A deep-based approach is proposed to obtain consistent and homogeneous biophysical parameter times series for MowEve detection. Recurrent Neural Networks are proposed as regression strategy allowing the synergistic integration of optical and Synthetic Aperture Radar data to reconstruct dense NDVI times series. Experimental results corroborates the interest of deriving consistent and homogeneous series of biophysical parameters for subsequent MowEve detection.

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Satellite-based monitoring of grassland: assessment of harvest dates and frequency using SAR

Cited by 5 publications

References 2 publications

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

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

Remote Sensing of Grassland Production and Management—A Review

Challenges in Grassland Mowing Event Detection with Multimodal Sentinel Images

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