Exploiting Time Series of Sentinel-1 and Sentinel-2 Imagery to Detect Meadow Phenology in Mountain Regions

Stendardi, Laura; Karlsen, Stein Rune; Niedrist, Georg; Gerdol, Renato; Zebisch, Marc; Rossi, Mattia; Notarnicola, Claudia

doi:10.3390/rs11050542

Cited by 76 publications

(55 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Previous studies have shown the potential of C and L-band SAR data to discriminate crop types [16,17], and a significant improvement in classification accuracy has been reported when SAR data and a Landsat8 satellite image time series were integrated [2,18]. Sentinel-1 data have been applied for mapping crop fields with the support of Sentinel-2 data or Landsat-8 data [19][20][21][22]. Besides these, TerraSAR-X and TanDEM-X provide X-band SAR data of high geometric accuracy at a high spatial resolution of 2.5-6 m in 30-km swaths in Stripmap mode [23].…”

Section: Introductionmentioning

confidence: 99%

Parcel-Based Crop Classification Using Multi-Temporal TerraSAR-X Dual Polarimetric Data

Sonobe

2019

Remote Sensing

View full text Add to dashboard Cite

Cropland maps are useful for the management of agricultural fields and the estimation of harvest yield. Some local governments have documented field properties, including crop type and location, based on site investigations. This process, which is generally done manually, is labor-intensive, and remote-sensing techniques can be used as alternatives. In this study, eight crop types (beans, beetroot, grass, maize, potatoes, squash, winter wheat, and yams) were identified using gamma naught values and polarimetric parameters calculated from TerraSAR-X (or TanDEM-X) dual-polarimetric (HH/VV) data. Three indices (difference (D-type), simple ratio (SR), and normalized difference (ND)) were calculated using gamma naught values and m-chi decomposition parameters and were evaluated in terms of crop classification. We also evaluated the classification accuracy of four widely used machine-learning algorithms (kernel-based extreme learning machine, support vector machine, multilayer feedforward neural network (FNN), and random forest) and two multiple-kernel methods (multiple kernel extreme learning machine (MKELM) and multiple kernel learning (MKL)). MKL performed best, achieving an overall accuracy of 92.1%, and proved useful for the identification of crops with small sample sizes. The difference (raw or normalized) between double-bounce scattering and odd-bounce scattering helped to improve the identification of squash and yams fields.

show abstract

Section: Introductionmentioning

confidence: 99%

Parcel-Based Crop Classification Using Multi-Temporal TerraSAR-X Dual Polarimetric Data

Sonobe

2019

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…These data, which are distributed free of charge, have an average revisit time of two days between 0 and 45 degrees latitude [18]. As a result, Sentinel-1 data have been widely used for land cover classification [19][20][21], monitoring of phenology [22], and biomass or production estimation. The interferometric wide-swath (IW) mode that offers VV (vertical transmit and receive) and VH (vertical transmit, horizontal receive) polarization data is normally used as the default acquisition mode [23,24].In addition to C-band SARs, the high sensitivity of the sigma naught of X-band sensors has been confirmed, and the potential of the X-band for identifying and forecasting crop growth using indices such as LAI has widely been confirmed [25,26].…”

mentioning

confidence: 99%

Combining ASNARO-2 XSAR HH and Sentinel-1 C-SAR VH/VV Polarization Data for Improved Crop Mapping

Sonobe

2019

Remote Sensing

View full text Add to dashboard Cite

The Advanced Satellite with New system ARchitecture for Observation-2 (ASNARO-2), which carries the X-band Synthetic Aperture Radar (XSAR), was launched on 17 January 2018 and is expected to be used to supplement data provided by larger satellites. Land cover classification is one of the most common applications of remote sensing, and the results provide a reliable resource for agricultural field management and estimating potential harvests. This paper describes the results of the first experiments in which ASNARO-2 XSAR data were applied for agricultural crop classification. In previous studies, Sentinel-1 C-SAR data have been widely utilized to identify crop types. Comparisons between ASNARO-2 XSAR and Sentinel-1 C-SAR using data obtained in June and August 2018 were conducted to identify five crop types (beans, beetroot, maize, potato, and winter wheat), and the combination of these data was also tested. To assess the potential for accurate crop classification, some radar vegetation indices were calculated from the backscattering coefficients for two dates. In addition, the potential of each type of SAR data was evaluated using four popular supervised learning models: Support vector machine (SVM), random forest (RF), multilayer feedforward neural network (FNN), and kernel-based extreme learning machine (KELM). The combination of ASNARO-2 XSAR and Sentinel-1 C-SAR data was effective, and overall classification accuracies of 85.4 ± 1.8% were achieved using SVM.

show abstract

“…RapidEye time series to classify PG management practices intensity are processed in [7]. S1 and S2 datasets are proposed for exploring correlation bewteen NDVI and σ 0 over PG in [5], and in [4] for monitoring MowEve and grazing occurrences. Some studies integrate optical multi-sensor data to increase the temporal resolution.…”

Section: B Methodological Analysis: Related Workmentioning

confidence: 99%

“…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]. The interest of S1 coherence variation for MowEve detection with 12-days pairs of images over PG in Estonia is also explored in [6].…”

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)

View full text Add to dashboard Cite

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.

show abstract

Exploiting Time Series of Sentinel-1 and Sentinel-2 Imagery to Detect Meadow Phenology in Mountain Regions

Cited by 76 publications

References 72 publications

Parcel-Based Crop Classification Using Multi-Temporal TerraSAR-X Dual Polarimetric Data

Parcel-Based Crop Classification Using Multi-Temporal TerraSAR-X Dual Polarimetric Data

Combining ASNARO-2 XSAR HH and Sentinel-1 C-SAR VH/VV Polarization Data for Improved Crop Mapping

Challenges in Grassland Mowing Event Detection with Multimodal Sentinel Images

Contact Info

Product

Resources

About