Recent Trend and Advance of Synthetic Aperture Radar with Selected Topics

Ouchi, Kazuo

doi:10.3390/rs5020716

Cited by 250 publications

(156 citation statements)

References 622 publications

(669 reference statements)

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“…While soil moisture estimation from Synthetic Aperture Radar (SAR) polarimetry (or scatterometer) data is a topic that has been investigated for over 30 years, with numerous papers having been written and statistical approaches developed, SAR and models using such data are nonetheless continuing to be re-configured, improved and extended given the wider availability of SAR data and to address a rapidly growing demand in its use in a broad set of industrial and environmental applications [1]. More reliable predictions of soil moisture are needed when optimizing crop water use and validating satellite remote-sensing/earth observational information [2,3]. Agricultural crop irrigation scheduling, disaster response and water management during droughts or flooding extreme events, soil erosion and pollution monitoring making use of hydrological models, all require reliable predictions of daily and field-scale soil moisture.…”

Section: Challenges In Modeling Soil Moisture Using Satellite Remotementioning

confidence: 99%

Statistical Modeling of Soil Moisture, Integrating Satellite Remote-Sensing (SAR) and Ground-Based Data

Hosseini¹,

Newlands

Dean

et al. 2015

Remote Sensing

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We present a flexible, integrated statistical-based modeling approach to improve the robustness of soil moisture data predictions. We apply this approach in exploring the consequence of different choices of leading predictors and covariates. Competing models, predictors, covariates and changing spatial correlation are often ignored in empirical analyses and validation studies. An optimal choice of model and predictors may, however, provide a more consistent and reliable explanation of the high environmental variability and stochasticity of soil moisture observational data. We integrate active polarimetric satellite remote-sensing data (RADARSAT-2, C-band) with ground-based in-situ data across an agricultural monitoring site in Canada. We apply a grouped step-wise algorithm to iteratively select best-performing predictors of soil moisture. Integrated modeling approaches may better account for observed uncertainty and be tuned to different applications that vary in scale and scope, while also providing greater insights into spatial scaling (upscaling and downscaling) of soil moisture variability from the field-to regional scale. We discuss several methodological extensions and data requirements to enable further statistical modeling and validation for improved agricultural decision-support.Remote Sens. 2015, 7 2753

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Section: Challenges In Modeling Soil Moisture Using Satellite Remotementioning

confidence: 99%

Statistical Modeling of Soil Moisture, Integrating Satellite Remote-Sensing (SAR) and Ground-Based Data

Hosseini¹,

Newlands

Dean

et al. 2015

Remote Sensing

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“…Some of the LiDAR metrics and the LiDAR-based biomass for the 201 forest stands were used to support the interpretation of the TDM interferometric signatures and to support the modeling phase relating the interferometric data to biomass (see Section 3). In Figure 2a, stand-level averages of the 95th percentile of LiDAR return values above a height threshold of 1.0 m or 10% of the maximum height (H95) have been plotted against the LiDAR-based biomass to assess the validity of the allometric function to be then used in the modeling phase, see Equation (3). Vegetation ratio derived from the LiDAR data [19], i.e., the ratio of LiDAR return values above a height threshold of 5.0 m and the total number of returns, provide information on canopy closure and can be considered as proxy for a similar parameter used in modeling, namely the area-fill factor (see Section 3).…”

Section: Field Observationsmentioning

confidence: 99%

Model-Based Biomass Estimation of a Hemi-Boreal Forest from Multitemporal TanDEM-X Acquisitions

et al. 2013

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Above-ground forest biomass is a significant variable in the terrestrial carbon budget, but is still estimated with relatively large uncertainty. Remote sensing methods can improve the characterization of the spatial distribution and estimation accuracy of biomass; in this respect, it is important to examine the potential offered by new sensors. To assess the contribution of the TanDEM-X mission, eighteen interferometric Synthetic Aperture Radar (SAR) image pairs acquired over the hemi-boreal test site of Remningstorp in Sweden were investigated. Three models were used for interpretation of TanDEM-X signatures and above-ground biomass retrieval: Interferometric Water Cloud Model (IWCM), Random Volume over Ground (RVoG) model, and a simple model based on penetration depth (PD). All use an allometric expression to relate above-ground biomass to forest height measured by TanDEM-X. The retrieval was assessed on 201 forest stands with a minimum size of 1 ha, and ranging from 6 to 267 Mg/ha (mean biomass of 105 Mg/ha) equally divided into a model training dataset and a validation test dataset. Biomass retrieved using the IWCM resulted in a Root Mean Square Error (RMSE) between 17% OPEN ACCESS Remote Sens. 2013, 5 5575 and 33%, depending on acquisition date and image acquisition geometry (angle of incidence, interferometric baseline, and orbit type). The RMSE in the case of the RVoG and the PD models were slightly higher. A multitemporal estimate of the above-ground biomass using all eighteen acquisitions resulted in an RMSE of 16% with R 2 = 0.93. These results prove the capability of TanDEM-X interferometric data to estimate forest aboveground biomass in the boreal zone.

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“…서 론 합성개구면레이다(SAR)는 보기 원하는 지형 표면의 고해상도 2차원 영상을 생성할 수 있는 기법이다. 이러한 SAR를 탑재한 SAR 위성은 군사, 민수 등 다양한 활용성 으로 인해 많은 위성 선진국들이 개발을 진행하고 있다 [1] .…”

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Method for Eliminating Spurious Signal from Deramped SAR Raw Data

Lim¹,

Ryu²

2016

The Journal of Korean Institute of Electromagnetic Engineering

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Deramping technique has been widely used to acquire high resolution SAR(Synthetic Aperture Radar) images for the advantage of the data size and the processing time. However, unwanted spurious signals caused by SAR hardware can be leaked in the process of converting into a digital signal through the ADC(Analog-Digital Converter) and added in a echo signal. These tones make image quality degrade significantly. In order to solve this problem, the unwanted tones need to be detected by analysing the characteristic of the noise tone and then effectively removed from raw data. In this paper, we propose a method for efficiently removing noise tone on the raw data based on the characteristic of spurious signals.

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Recent Trend and Advance of Synthetic Aperture Radar with Selected Topics

Cited by 250 publications

References 622 publications

Statistical Modeling of Soil Moisture, Integrating Satellite Remote-Sensing (SAR) and Ground-Based Data

Statistical Modeling of Soil Moisture, Integrating Satellite Remote-Sensing (SAR) and Ground-Based Data

Model-Based Biomass Estimation of a Hemi-Boreal Forest from Multitemporal TanDEM-X Acquisitions

Method for Eliminating Spurious Signal from Deramped SAR Raw Data

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