An introduction to the RADARSAT-2 mission

Morena, L C; James, Kenny; Beck, J. B.

doi:10.5589/m04-004

Cited by 150 publications

(67 citation statements)

References 2 publications

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“…The Canadian satellite Radarsat-2 was launched in December 2007 [45]. It has a SAR instrument operating at C-band (f = 5.405 GHz, λ = 5.5 cm).…”

Section: Radar Datamentioning

confidence: 99%

Monitoring Wheat and Rapeseed by Using Synchronous Optical and Radar Satellite Data—From Temporal Signatures to Crop Parameters Estimation

Fieuzal¹,

Baup²,

Marais-Sicre³

2013

ARS

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This paper investigates the sensitivity of multi-temporal SAR data acquired at different frequencies (X-, C-and L-bands), polarizations (HH, VV, VH and HV) and incidence angles (from 24˚ to 53˚) during the growing season of two winter crops (rapeseed and wheat). This study was part of a multi-sensor crop-monitoring experiment that was performed from February to November 2010 (MCM'10). During the experiment, dense series of satellite data were acquired in microwave, optical and thermal domains (more than 150 images were provided by TerraSAR-X, Radarsat-2 Alos, Formosat-2, Spot-4/5 and Landsat-5/7) were synchronous with ground measurements over an agricultural area located in southwestern France, near Toulouse. An angular normalization of radar signals is first performed for each crop type at X-and C-bands by using a dense temporal satellite series and the complementarity provided by microwave and optical data. The results show that the angular sensitivity of radar backscatter decreases with the increase of the vegetation index (from 0.4 dB.˚− 1 over bare soils to 0.05 dB.˚− 1 for fully vegetated fields). Lower angular sensitivity is observed at X-band (compared to C-band), and for the cross-polarized signal. Analyses of the temporal signatures of the radar backscatter show a well-marked signal dynamic at X-, C-and L-bands, depending on the crops and theirs associated phenological stages. During the stems elongation of wheat while the NDVI increases of 0.2, a dynamic of 10 dB is observed at X-band and at C-band with VV polarization. Interesting behaviors are also observed during the crop senescence with an increase of several dB (depending on the sensor configuration), while the NDVI decreases of 0.5. Over rapeseed, cross-polarized backscatters offer promising dynamic of 6 dB during the seed development, while the NDVI saturates at maximum values. The use of radar signals, in complement of optical, for crop parameters monitoring is achieved in terms of leaf area index and crop height estimations. Over rapeseed, best correlations between crop parameters and radar signals are obtained at C-band, by combining co-and cross-polarized backscatters (R 2 > 0.61). Over wheat, best results are achieved by using X-band data (R 2 > 0.64).

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“…The Canadian satellite Radarsat-2 was launched in December 2007 [45]. It has a SAR instrument operating at C-band (f = 5.405 GHz, λ = 5.5 cm).…”

Section: Radar Datamentioning

confidence: 99%

Monitoring Wheat and Rapeseed by Using Synchronous Optical and Radar Satellite Data—From Temporal Signatures to Crop Parameters Estimation

Fieuzal¹,

Baup²,

Marais-Sicre³

2013

ARS

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“…Several validation studies provided insights into the reliability and robustness of the soil moisture estimates from SMOS [2,[27][28][29] and over India [30]. RADAR SATellite-2 (RADARSAT-2) is an active microwave satellite mission operating in C-band, capable of retrieving surface soil moisture at a spatial resolution of less than 100 m and with a temporal resolution of 24 days [31]. Launched in December 2007, RADARSAT-2 provides a long term dataset for the validation studies.…”

Section: Introductionmentioning

confidence: 99%

MAPSM: A Spatio-Temporal Algorithm for Merging Soil Moisture from Active and Passive Microwave Remote Sensing

et al. 2016

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Simple Summary: -A novel algorithm delivering high resolution soil moisture maps is developed by merging active (SAR) and passive microwave.-MAPSM is based on the concept of Water Change Capacity. -A case study using MAPSM is presented by using the RADARSAT-2 and SMOS retrieved soil moisture data products over Berambadi watershed, Karnataka, India. -The algorithm parameters show scalability from the spatial resolution of 20 m to 2000 m.Abstract: Availability of soil moisture observations at a high spatial and temporal resolution is a prerequisite for various hydrological, agricultural and meteorological applications. In the current study, a novel algorithm for merging soil moisture from active microwave (SAR) and passive microwave is presented. The MAPSM algorithm-Merge Active and Passive microwave Soil Moisture-uses a spatio-temporal approach based on the concept of the Water Change Capacity (WCC) which represents the amplitude and direction of change in the soil moisture at the fine spatial resolution. The algorithm is applied and validated during a period of 3 years spanning from 2010 to 2013 over the Berambadi watershed which is located in a semi-arid tropical region in the Karnataka state of south India. Passive microwave products are provided from ESA Level 2 soil moisture products derived from Soil Moisture and Ocean Salinity (SMOS) satellite (3 days temporal resolution and 40 km nominal spatial resolution). Active microwave are based on soil moisture retrievals from 30 images of RADARSAT-2 data (24 days temporal resolution and 20 m spatial resolution). The results show that MAPSM is able to provide a good estimate of soil moisture at a spatial resolution of 500 m with an RMSE of 0.025 m 3 /m 3 and 0.069 m 3 /m 3 when comparing it to soil moisture from RADARSAT-2 and in-situ measurements, respectively. The use of Sentinel-1 and RISAT products in MAPSM algorithm is envisioned over other areas where high number of revisits is available. This will need an update of the algorithm to take into account the angle sampling and resolution of Sentinel-1 and RISAT data.

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“…Operated at C-band, RS2 is a satellite of the Canadian Space Agency (CSA), which offers a wavelength of 5.5 cm [53]. Studies have shown RS2 and its predecessor, RADARSAT-1, to be applicable for many purposes, but limitations concerning vegetation mapping are well documented [54].…”

Section: Remote Sensing Datamentioning

confidence: 99%

“…Studies have shown RS2 and its predecessor, RADARSAT-1, to be applicable for many purposes, but limitations concerning vegetation mapping are well documented [54]. The available data is acquired in Standard Beam mode, at VV-VH polarization, and, after multi-looking, we approximate the recommended operational resolution at 20 m [53].…”

Section: Remote Sensing Datamentioning

confidence: 99%

Evaluation of Multi-Frequency SAR Images for Tropical Land Cover Mapping

Hagensieker

Waske

2018

Remote Sensing

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Earth Observation (EO) data plays a major role in supporting surveying compliance of several multilateral environmental treaties, such as UN-REDD+ (United Nations Reducing Emissions from Deforestation and Degradation). In this context, land cover maps of remote sensing data are the most commonly used EO products and development of adequate classification strategies is an ongoing research topic. However, the availability of meaningful multispectral data sets can be limited due to cloud cover, particularly in the tropics. In such regions, the use of SAR systems (Synthetic Aperture Radar), which are nearly independent form weather conditions, is particularly promising.With an ever-growing number of SAR satellites, as well as the increasing accessibility of SAR data, potentials for multi-frequency remote sensing are becoming numerous. In our study, we evaluate the synergistic contribution of multitemporal L-, C-, and X-band data to tropical land cover mapping. We compare classification outcomes of ALOS-2, RADARSAT-2, and TerraSAR-X datasets for a study site in the Brazilian Amazon using a wrapper approach. After preprocessing and calculation of GLCM texture (Grey Level Co-Occurence), the wrapper utilizes Random Forest classifications to estimate scene importance. Comparing the contribution of different wavelengths, ALOS-2 data perform best in terms of overall classification accuracy, while the classification of TerraSAR-X data yields higher accuracies when compared to the results achieved by RADARSAT-2. Moreover, the wrapper underlines potentials of multi-frequency classification as integration of multi-frequency images is always preferred over multi-temporal, mono-frequent composites. We conclude that, despite distinct advantages of certain sensors, for land cover classification, multi-sensoral integration is beneficial.

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An introduction to the RADARSAT-2 mission

Cited by 150 publications

References 2 publications

Monitoring Wheat and Rapeseed by Using Synchronous Optical and Radar Satellite Data—From Temporal Signatures to Crop Parameters Estimation

Monitoring Wheat and Rapeseed by Using Synchronous Optical and Radar Satellite Data—From Temporal Signatures to Crop Parameters Estimation

MAPSM: A Spatio-Temporal Algorithm for Merging Soil Moisture from Active and Passive Microwave Remote Sensing

Evaluation of Multi-Frequency SAR Images for Tropical Land Cover Mapping

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