Synthetic Aperture Radar (SAR) image processing for operational space-based agriculture mapping

Robertson, Laura Dingle; Davidson, Andrew; McNairn, Heather; Hosseini, Mehdi; Mitchell, Scott; Abelleyra, Diego de; Verón, Santiago R.; Cosh, Michael H.

doi:10.1080/01431161.2020.1754494

Cited by 31 publications

(11 citation statements)

References 72 publications

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“…A Gamma Map speckle filter with a 9 by 9 filter size is applied to suppress speckle noise [27]. The data are then terrain corrected and co-registered using the Range Doppler algorithm with Shuttle Radar Topography Mission (SRTM) 1 arcsecond Digital Elevation Model (DEM) and resampled to 30 m pixel spacing [28,29]. For making comparisons with the ACI, PALSAR-2 data were re-projected to the UTM 14N grids of the ACI using bilinear interpolation.…”

Section: Aafc Annual Crop Inventory (Aci)mentioning

confidence: 99%

Comparison between Dense L-Band and C-Band Synthetic Aperture Radar (SAR) Time Series for Crop Area Mapping over a NISAR Calibration-Validation Site

et al. 2021

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Crop area mapping is important for tracking agricultural production and supporting food security. Spaceborne approaches using synthetic aperture radar (SAR) now allow for mapping crop area at moderate spatial and temporal resolutions. Multi-frequency SAR data is highly useful for crop monitoring because backscatter response from vegetation canopies is wavelength dependent. This study evaluates the utility of C-band Sentinel-1B (Sentinel-1) and L-band ALOS-2 (PALSAR) data, collected during the 2019 growing season, for generating accurate active crop extent (crop vs. non-crop) classifications over an agricultural region in western Canada. Evaluations were performed against the Agriculture and Agri-Food Canada satellite-based Annual Cropland Inventory (ACI), an open data product that maps land cover across the extent of Canada’s agricultural land. Classifications were performed using the temporal coefficient of variation (CV) approach, where an optimal crop/non-crop delineating CV threshold (CVthr) is selected according to Youden’s J-statistic. Results show that crop area mapping agreed better with the ACI when using Sentinel-1 data (83.5%) compared to PALSAR (73.2%). Analysis of performance by crop reveals that PALSAR’s poorer performance can be attributed to soybean, urban, grassland, and pasture ACI classes. This study also compared CV values to in situ wet biomass data for canola and soybeans, showing that crops with lower biomass (soybean) had correspondingly lower CV values.

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Section: Aafc Annual Crop Inventory (Aci)mentioning

confidence: 99%

Comparison between Dense L-Band and C-Band Synthetic Aperture Radar (SAR) Time Series for Crop Area Mapping over a NISAR Calibration-Validation Site

et al. 2021

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“…[22] Although some geometric-phase metasurfaces can realize parallel phase profiles as well as dispersion profile manipulation, these devices suffer from poor broad bandwidth performance. [23][24][25][26] Focus-scanning technology realized by an individual metadevice has important applications, such as multiple modes of phased-array radar scanning, [27] synthetic aperture radar (SAR) imaging, [28][29][30][31] sensing, and communications. [32,33] However, the reported focusing meta-devices [34,35] usually work only at a target frequency or a particular range of frequencies; therefore, these cannot achieve a high broad bandwidth performance.…”

Section: Introductionmentioning

confidence: 99%

Frequency‐Controlled Focusing Using Achromatic Metasurface

Zheng

Cai

et al. 2020

Advanced Optical Materials

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Frequency‐controlled metasurfaces have attracted significant attention for a variety of applications, such as radar imaging and 5G communication, owing to their small size and low cost. However, due to the inherent material dispersion of these devices, bandwidth is an essential requirement for their controlling their wavefront. To evaluate the broadband performance of frequency‐controlled metasurface focusing systems, a multiresonant model is used to achieve controlled manipulation of frequency‐splitting and focusing reflective metasurface; further, the transition from broadband disorder scattering to user‐desired reflection is obtained. It is shown that the focusing position can be controlled by the careful design of each unit cell. As a proof of concept, a broadband focusing metasurface with frequency‐splitting effect over the entire X band, i.e., 8–12 GHz, is designed and experimentally demonstrated, which can control the position of focal spots with different wavelengths by its achromatic and reflective characteristics. It is believed that the proposed metasurface will provide useful insights for wavefront manipulation in broadband and chromatically corrected imaging systems.

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“…Each data source captures different crop properties useful for crop type mapping. For example, optical imagery records information that can be used to characterize a crop's phenology, while SAR imagery captures properties of the canopy structure that may signify differences between crops [22]. We processed and extracted both optical and SAR data to the survey plot locations for maize area mapping.…”

Section: Earth Observation Datamentioning

confidence: 99%

Understanding the Requirements for Surveys to Support Satellite-Based Crop Type Mapping: Evidence from Sub-Saharan Africa

Azzari,

Jain,

Jeffries

et al. 2021

Remote Sensing

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This paper provides recommendations on how large-scale household surveys should be conducted to generate the data needed to train models for satellite-based crop type mapping in smallholder farming systems. The analysis focuses on maize cultivation in Malawi and Ethiopia, and leverages rich, georeferenced plot-level data from national household surveys that were conducted in 2018–20 and integrated with Sentinel-2 satellite imagery and complementary geospatial data. To identify the approach to survey data collection that yields optimal data for training remote sensing models, 26,250 in silico experiments are simulated within a machine learning framework. The best model is then applied to map seasonal maize cultivation from 2016 to 2019 at 10-m resolution in both countries. The analysis reveals that smallholder plots with maize cultivation can be identified with up to 75% accuracy. Collecting full plot boundaries or complete plot corner points provides the best quality of information for model training. Classification performance peaks with slightly less than 60% of the training data. Seemingly little erosion in accuracy under less preferable approaches to georeferencing plots results in the total area under maize cultivation being overestimated by 0.16–0.47 million hectares (8–24%) in Malawi.

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Synthetic Aperture Radar (SAR) image processing for operational space-based agriculture mapping

Cited by 31 publications

References 72 publications

Comparison between Dense L-Band and C-Band Synthetic Aperture Radar (SAR) Time Series for Crop Area Mapping over a NISAR Calibration-Validation Site

Comparison between Dense L-Band and C-Band Synthetic Aperture Radar (SAR) Time Series for Crop Area Mapping over a NISAR Calibration-Validation Site

Frequency‐Controlled Focusing Using Achromatic Metasurface

Understanding the Requirements for Surveys to Support Satellite-Based Crop Type Mapping: Evidence from Sub-Saharan Africa

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